金融大數據分析_week7作業_深度學習ANN、CNN.ipynb

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      "collapsed_sections": [
        "x_PXsNarENRB",
        "e2FmBLUeDhS9"
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      "authorship_tag": "ABX9TyOjHdqAJj6s7WhcGfVzMNmm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Kimeteor309/Financial-big-data-analysis/blob/main/%E9%87%91%E8%9E%8D%E5%A4%A7%E6%95%B8%E6%93%9A%E5%88%86%E6%9E%90_week7%E4%BD%9C%E6%A5%AD_%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92ANN%E3%80%81CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **在 Colab 上使用中文字體**"
      ],
      "metadata": {
        "id": "RXhti_Y2D--4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Colab 進行matplotlib繪圖時顯示繁體中文\n",
        "# 下載台北思源黑體並命名taipei_sans_tc_beta.ttf，移至指定路徑\n",
        "!wget -O TaipeiSansTCBeta-Regular.ttf https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_&export=download\n",
        "\n",
        "import matplotlib\n",
        "\n",
        "# 改style要在改font之前\n",
        "# plt.style.use('seaborn')\n",
        "\n",
        "matplotlib.font_manager.fontManager.addfont('TaipeiSansTCBeta-Regular.ttf')\n",
        "matplotlib.rc('font', family='Taipei Sans TC Beta')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2rPWviFECf3",
        "outputId": "87c043a2-a4d5-416d-fbca-7b110cf142a7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-10-25 13:22:51--  https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_\n",
            "Resolving drive.google.com (drive.google.com)... 74.125.197.139, 74.125.197.102, 74.125.197.100, ...\n",
            "Connecting to drive.google.com (drive.google.com)|74.125.197.139|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://drive.usercontent.google.com/download?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_ [following]\n",
            "--2024-10-25 13:22:51--  https://drive.usercontent.google.com/download?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 74.125.20.132, 2607:f8b0:400e:c07::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|74.125.20.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20659344 (20M) [application/octet-stream]\n",
            "Saving to: ‘TaipeiSansTCBeta-Regular.ttf’\n",
            "\n",
            "TaipeiSansTCBeta-Re 100%[===================>]  19.70M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2024-10-25 13:22:56 (133 MB/s) - ‘TaipeiSansTCBeta-Regular.ttf’ saved [20659344/20659344]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **取得股價資料**"
      ],
      "metadata": {
        "id": "x_PXsNarENRB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 將民國年轉換為西元年\n",
        "def convert_to_gregorian(date_str):\n",
        "    year, month, day = date_str.split('/')\n",
        "    year = str(int(year) + 1911)  # 民國年轉換為西元年\n",
        "    return f'{year}/{month}/{day}'\n",
        "\n",
        "# 從台灣證券交易所 API 獲取每日收盤價\n",
        "def get_twse_data(stock_id, date):\n",
        "    url = f\"https://www.twse.com.tw/exchangeReport/STOCK_DAY?response=json&date={date}&stockNo={stock_id}\"\n",
        "    response = requests.get(url)\n",
        "    data = response.json()\n",
        "\n",
        "    # 解析 JSON 並轉換為 DataFrame\n",
        "    columns = data['fields']\n",
        "    rows = data['data']\n",
        "    df = pd.DataFrame(rows, columns=columns)\n",
        "\n",
        "    # 將日期轉換為西元年\n",
        "    df['日期'] = df['日期'].apply(convert_to_gregorian)\n",
        "    df['日期'] = pd.to_datetime(df['日期'], format='%Y/%m/%d')  # 指定西元年格式\n",
        "\n",
        "    # 只保留你需要的欄位，並重新命名為英文字段\n",
        "    df = df[['日期', '開盤價', '最高價', '最低價', '收盤價', '成交股數']].copy()\n",
        "    df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']\n",
        "\n",
        "    # 將數值欄位轉為數字類型，並去除逗號\n",
        "    df['Open'] = pd.to_numeric(df['Open'].str.replace(',', ''))\n",
        "    df['High'] = pd.to_numeric(df['High'].str.replace(',', ''))\n",
        "    df['Low'] = pd.to_numeric(df['Low'].str.replace(',', ''))\n",
        "    df['Close'] = pd.to_numeric(df['Close'].str.replace(',', ''))\n",
        "    df['Volume'] = pd.to_numeric(df['Volume'].str.replace(',', ''))\n",
        "\n",
        "    return df\n",
        "\n",
        "# 獲取一段日期範圍內的股價資料\n",
        "def get_data_range(stock_id, start_year, end_year, start_month, end_month):\n",
        "    all_data = pd.DataFrame()  # 用於存放所有年份和月份的數據\n",
        "\n",
        "    # 迭代年份\n",
        "    for year in range(start_year, end_year + 1):\n",
        "        # 迭代月份\n",
        "        for month in range(start_month, end_month + 1):\n",
        "            # 日期格式：YYYYMMDD，只需要指定月份的第一天\n",
        "            date = f\"{year}{month:02d}01\"\n",
        "            # 獲取指定月份的數據\n",
        "            df_month = get_twse_data(stock_id, date)\n",
        "            # 合併當前月份數據到所有數據中\n",
        "            all_data = pd.concat([all_data, df_month], ignore_index=True)  # 合併時確保索引正確\n",
        "\n",
        "    return all_data\n",
        "\n",
        "# 取得台積電從2020年至2023年的股價資料\n",
        "stock_id = '2330'  # 台積電股票代號\n",
        "df = get_data_range(stock_id, 2020, 2023, 1, 12)\n",
        "\n",
        "# 顯示資料\n",
        "df"
      ],
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        "id": "5mCnvjUFET-Q",
        "outputId": "6b91250a-769b-4b4e-9da7-ebd8035f3abc"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
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              "          Date   Open   High    Low  Close    Volume\n",
              "0   2020-01-02  332.5  339.0  332.5  339.0  33282120\n",
              "1   2020-01-03  344.0  345.0  335.5  339.5  42023268\n",
              "2   2020-01-06  333.0  334.5  332.0  332.0  45677057\n",
              "3   2020-01-07  332.5  333.0  326.5  329.5  51746181\n",
              "4   2020-01-08  325.0  333.0  325.0  329.5  37913748\n",
              "..         ...    ...    ...    ...    ...       ...\n",
              "969 2023-12-25  582.0  585.0  580.0  581.0   9994163\n",
              "970 2023-12-26  583.0  586.0  582.0  586.0  17033926\n",
              "971 2023-12-27  587.0  592.0  586.0  592.0  34750958\n",
              "972 2023-12-28  592.0  593.0  589.0  593.0  27152352\n",
              "973 2023-12-29  589.0  593.0  589.0  593.0  20155619\n",
              "\n",
              "[974 rows x 6 columns]"
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
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              "      <td>2020-01-03</td>\n",
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              "      <td>2020-01-07</td>\n",
              "      <td>332.5</td>\n",
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              "      <td>2020-01-08</td>\n",
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              "      <th>969</th>\n",
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              "      <td>592.0</td>\n",
              "      <td>593.0</td>\n",
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              "\n",
              "  <div id=\"id_187defbc-8357-4315-abb3-604eb2593086\">\n",
              "    <style>\n",
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              "      .colab-df-generate:hover {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 974,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-01-02 00:00:00\",\n        \"max\": \"2023-12-29 00:00:00\",\n        \"num_unique_values\": 974,\n        \"samples\": [\n          \"2020-10-29 00:00:00\",\n          \"2022-03-24 00:00:00\",\n          \"2020-09-21 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98.61845073632516,\n        \"min\": 252.0,\n        \"max\": 685.0,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          501.0,\n          678.0,\n          639.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98.96023252602356,\n        \"min\": 253.0,\n        \"max\": 688.0,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          584.0,\n          291.0,\n          330.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98.04648027339752,\n        \"min\": 235.5,\n        \"max\": 678.0,\n        \"num_unique_values\": 376,\n        \"samples\": [\n          433.0,\n          442.5,\n          567.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98.60767693578572,\n        \"min\": 248.0,\n        \"max\": 683.0,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          635.0,\n          276.5,\n          332.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22509517,\n        \"min\": 7898012,\n        \"max\": 172157746,\n        \"num_unique_values\": 974,\n        \"samples\": [\n          43075387,\n          20769676,\n          40193920\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **作業1：使用 TensorFlow 或 PyTorch 實作類神經網路模型預測股票價格**\n",
        "\n",
        "（TensorFlow 使用 Dense layer；PyTorch 使用 Linear layer）。\n",
        "\n",
        "說明：使用收盤價或技術指標預測，預測N日後的股價。"
      ],
      "metadata": {
        "id": "e2FmBLUeDhS9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **使用收盤價預測**"
      ],
      "metadata": {
        "id": "SDu-QJ8VK8le"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "_5m2Yvb6DcYl"
      },
      "outputs": [],
      "source": [
        "# 資料預處理\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df = df.sort_values('Date')\n",
        "\n",
        "# 取出特徵和目標變量\n",
        "data = df[['Close']].values\n",
        "\n",
        "# 正規化數據（將數據縮放至 [0, 1] 之間）\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "scaled_data = scaler.fit_transform(data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 創建訓練和測試數據\n",
        "# 設定 Lookback 天數（例如使用前 60 天的數據預測未來一天）\n",
        "lookback = 60\n",
        "\n",
        "# 創建訓練集\n",
        "train_size = int(len(scaled_data) * 0.8)\n",
        "train_data = scaled_data[:train_size]\n",
        "test_data = scaled_data[train_size - lookback:]\n",
        "\n",
        "\n",
        "# 創建訓練集的特徵和目標\n",
        "def create_dataset(data, lookback):\n",
        "    X, y = [], []\n",
        "    for i in range(lookback, len(data)):\n",
        "        X.append(data[i-lookback:i, 0])\n",
        "        y.append(data[i, 0])\n",
        "    return np.array(X), np.array(y)\n",
        "\n",
        "X_train, y_train = create_dataset(train_data, lookback)\n",
        "\n",
        "# Reshape X 以符合 LSTM 的輸入要求：[樣本數, lookback, 特徵數]\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
      ],
      "metadata": {
        "id": "sdcwLtFdHANu"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 建立和訓練模型\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense, Dropout, BatchNormalization\n",
        "\n",
        "# 建立模型\n",
        "model = Sequential()\n",
        "\n",
        "# LSTM 層\n",
        "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
        "model.add(Dropout(0.2))  # 防止過擬合\n",
        "\n",
        "\n",
        "model.add(LSTM(units=50, return_sequences=False))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# 輸出層\n",
        "model.add(Dense(units=1))\n",
        "\n",
        "# 編譯模型\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "# 訓練模型\n",
        "model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66xLreFSHQh7",
        "outputId": "8e85d1b4-92ee-41c1-a964-66d130111383"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 80ms/step - loss: 0.2769 - val_loss: 0.0077\n",
            "Epoch 2/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 59ms/step - loss: 0.0203 - val_loss: 0.0082\n",
            "Epoch 3/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0096 - val_loss: 0.0064\n",
            "Epoch 4/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 63ms/step - loss: 0.0080 - val_loss: 0.0044\n",
            "Epoch 5/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0071 - val_loss: 0.0041\n",
            "Epoch 6/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - loss: 0.0073 - val_loss: 0.0041\n",
            "Epoch 7/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 73ms/step - loss: 0.0055 - val_loss: 0.0041\n",
            "Epoch 8/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 93ms/step - loss: 0.0056 - val_loss: 0.0042\n",
            "Epoch 9/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 59ms/step - loss: 0.0065 - val_loss: 0.0039\n",
            "Epoch 10/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0060 - val_loss: 0.0038\n",
            "Epoch 11/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0064 - val_loss: 0.0053\n",
            "Epoch 12/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0060 - val_loss: 0.0044\n",
            "Epoch 13/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - loss: 0.0044 - val_loss: 0.0040\n",
            "Epoch 14/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0056 - val_loss: 0.0036\n",
            "Epoch 15/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - loss: 0.0057 - val_loss: 0.0036\n",
            "Epoch 16/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0049 - val_loss: 0.0041\n",
            "Epoch 17/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 70ms/step - loss: 0.0051 - val_loss: 0.0040\n",
            "Epoch 18/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 98ms/step - loss: 0.0049 - val_loss: 0.0034\n",
            "Epoch 19/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 58ms/step - loss: 0.0059 - val_loss: 0.0034\n",
            "Epoch 20/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0047 - val_loss: 0.0033\n",
            "Epoch 21/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0052 - val_loss: 0.0034\n",
            "Epoch 22/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0046 - val_loss: 0.0032\n",
            "Epoch 23/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0049 - val_loss: 0.0033\n",
            "Epoch 24/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 0.0047 - val_loss: 0.0033\n",
            "Epoch 25/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0052 - val_loss: 0.0031\n",
            "Epoch 26/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 0.0047 - val_loss: 0.0030\n",
            "Epoch 27/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 74ms/step - loss: 0.0046 - val_loss: 0.0036\n",
            "Epoch 28/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 81ms/step - loss: 0.0045 - val_loss: 0.0033\n",
            "Epoch 29/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 58ms/step - loss: 0.0043 - val_loss: 0.0041\n",
            "Epoch 30/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0041 - val_loss: 0.0029\n",
            "Epoch 31/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0044 - val_loss: 0.0028\n",
            "Epoch 32/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0045 - val_loss: 0.0029\n",
            "Epoch 33/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0046 - val_loss: 0.0040\n",
            "Epoch 34/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0037 - val_loss: 0.0028\n",
            "Epoch 35/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 67ms/step - loss: 0.0042 - val_loss: 0.0027\n",
            "Epoch 36/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 95ms/step - loss: 0.0040 - val_loss: 0.0030\n",
            "Epoch 37/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 59ms/step - loss: 0.0040 - val_loss: 0.0027\n",
            "Epoch 38/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 0.0042 - val_loss: 0.0028\n",
            "Epoch 39/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 0.0035 - val_loss: 0.0038\n",
            "Epoch 40/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0048 - val_loss: 0.0029\n",
            "Epoch 41/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0038 - val_loss: 0.0032\n",
            "Epoch 42/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0040 - val_loss: 0.0025\n",
            "Epoch 43/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0043 - val_loss: 0.0028\n",
            "Epoch 44/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 0.0034 - val_loss: 0.0026\n",
            "Epoch 45/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 90ms/step - loss: 0.0037 - val_loss: 0.0029\n",
            "Epoch 46/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 68ms/step - loss: 0.0041 - val_loss: 0.0024\n",
            "Epoch 47/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 58ms/step - loss: 0.0040 - val_loss: 0.0031\n",
            "Epoch 48/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0035 - val_loss: 0.0023\n",
            "Epoch 49/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0045 - val_loss: 0.0026\n",
            "Epoch 50/50\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0037 - val_loss: 0.0023\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7fd63f589e40>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 預測測試集，使用 train_data 的最後 lookback 天作為初始窗口\n",
        "def sliding_window_prediction(model, train_data, test_data, lookback):\n",
        "    predictions = []\n",
        "\n",
        "    # 初始窗口使用 train_data 的最後 lookback 天數據\n",
        "    current_window = train_data[-lookback:]\n",
        "\n",
        "    for i in range(len(test_data)):\n",
        "        # 用當前的窗口數據進行預測\n",
        "        current_window = current_window.reshape(1, lookback, 1)  # Reshape to match LSTM input\n",
        "\n",
        "        predicted_price = model.predict(current_window)  # 預測當前的價格\n",
        "        predictions.append(predicted_price[0, 0])  # 添加預測結果\n",
        "\n",
        "        # 更新滑動窗口，把窗口向右滑動\n",
        "        current_window = np.append(current_window[0, 1:], test_data[i])  # 滑動一個步長，更新窗口\n",
        "\n",
        "    return predictions\n",
        "\n",
        "# 使用改進的滑動窗口進行預測\n",
        "predicted_prices = sliding_window_prediction(model, train_data, test_data, lookback)\n",
        "\n",
        "# 還原價格\n",
        "predicted_prices = scaler.inverse_transform(np.array(predicted_prices).reshape(-1, 1))\n",
        "\n",
        "# 檢查形狀\n",
        "print(\"Predicted prices shape:\", predicted_prices.shape)\n",
        "print(\"Test data shape:\", test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSIXZHdmB-ZQ",
        "outputId": "cbcc9995-c156-49b1-f6e4-4e65f6285177"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
            "Predicted prices shape: (255, 1)\n",
            "Test data shape: (255, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 視覺化結果\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# 創建日期範圍（根據你的數據日期）\n",
        "dates = pd.to_datetime(df['Date'])\n",
        "train_size = len(X_train)  # 訓練集大小\n",
        "\n",
        "# 訓練集的日期和價格\n",
        "train_dates = dates[:train_size]\n",
        "train_prices = df['Close'][:train_size]\n",
        "\n",
        "# 測試集的日期和價格\n",
        "test_dates = dates[train_size:]\n",
        "test_prices = df['Close'][train_size:]\n",
        "\n",
        "\n",
        "# 開始繪圖\n",
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(train_dates, train_prices, label='Training Data (True Price)', color='blue')\n",
        "plt.plot(test_dates, test_prices, label='Test Data (True Price)', color='orange')\n",
        "plt.plot(test_dates, predicted_prices, label='Predicted Price', color='green')\n",
        "\n",
        "plt.title('2330.TW Stock Price Prediction')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.legend(loc='best')\n",
        "\n",
        "# 顯示圖表\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "H_zRyz-LIM0G",
        "outputId": "936e4d56-b160-42c0-98df-9033babf23bf"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **作業2：使用 TensorFlow 或 PyTorch 實作 CNN 捕捉 K 線趨勢，並預測股價漲跌（分類任務）。**\n",
        "\n",
        "說明：輸入一張 N 天的 K 線圖判斷未來價格可能漲或跌，label 方式可以使用技術指標輔助，\n",
        "\n",
        "例如：5日均線高於10日均線標註「漲」，5日均線低於10日均線標註「跌」。"
      ],
      "metadata": {
        "id": "mpU32v_lZhS2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 設定 Date 欄位為索引\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df.set_index('Date', inplace=True)\n",
        "\n",
        "# 計算 5 日和 10 日移動平均線\n",
        "df['MA_5'] = df['Close'].rolling(window=5).mean()\n",
        "df['MA_10'] = df['Close'].rolling(window=10).mean()\n",
        "\n",
        "# 標註漲跌\n",
        "df['Label'] = df.apply(lambda row: '漲' if row['MA_5'] > row['MA_10'] else '跌', axis=1)\n",
        "\n",
        "# 移除 NaN\n",
        "df.dropna(inplace=True)\n",
        "\n",
        "print(df[['Close', 'MA_5', 'MA_10', 'Label']])\n"
      ],
      "metadata": {
        "id": "LgvNxvHWbZkX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b0b89d5-272d-4bf6-aaf1-2139751f88a7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Close   MA_5   MA_10 Label\n",
            "Date                                  \n",
            "2020-01-15  340.0  340.9  337.40     漲\n",
            "2020-01-16  334.5  340.3  336.95     漲\n",
            "2020-01-17  333.0  339.0  336.30     漲\n",
            "2020-01-20  333.0  337.3  336.40     漲\n",
            "2020-01-30  316.5  331.4  335.10     跌\n",
            "...           ...    ...     ...   ...\n",
            "2023-12-22  582.0  582.8  581.00     漲\n",
            "2023-12-25  581.0  582.0  581.70     漲\n",
            "2023-12-26  586.0  582.2  582.50     跌\n",
            "2023-12-27  592.0  583.6  584.00     跌\n",
            "2023-12-28  593.0  586.8  585.10     漲\n",
            "\n",
            "[964 rows x 4 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-1e1993f34b6d>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Date'] = pd.to_datetime(df['Date'])\n",
            "<ipython-input-7-1e1993f34b6d>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['MA_5'] = df['Close'].rolling(window=5).mean()\n",
            "<ipython-input-7-1e1993f34b6d>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['MA_10'] = df['Close'].rolling(window=10).mean()\n",
            "<ipython-input-7-1e1993f34b6d>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Label'] = df.apply(lambda row: '漲' if row['MA_5'] > row['MA_10'] else '跌', axis=1)\n",
            "<ipython-input-7-1e1993f34b6d>:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.dropna(inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install mplfinance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ut2Zv1ghkZRm",
        "outputId": "4aebb49d-e2db-49eb-da80-560a5d9fd853"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting mplfinance\n",
            "  Downloading mplfinance-0.12.10b0-py3-none-any.whl.metadata (19 kB)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mplfinance) (3.7.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from mplfinance) (2.2.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (1.4.7)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->mplfinance) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->mplfinance) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->mplfinance) (1.16.0)\n",
            "Downloading mplfinance-0.12.10b0-py3-none-any.whl (75 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/75.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.0/75.0 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: mplfinance\n",
            "Successfully installed mplfinance-0.12.10b0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "import os\n",
        "\n",
        "# 指定資料夾路徑\n",
        "img_dir = 'kline_images'\n",
        "\n",
        "# 檢查資料夾是否存在並清空\n",
        "if os.path.exists(img_dir):\n",
        "    shutil.rmtree(img_dir)  # 刪除資料夾及其內容\n",
        "os.makedirs(img_dir)  # 重新創建空資料夾"
      ],
      "metadata": {
        "id": "dPJ5nvPPnUwG"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 每個標籤建立子資料夾\n",
        "for label in ['漲', '跌']:\n",
        "    os.makedirs(os.path.join(img_dir, label), exist_ok=True)\n",
        "\n",
        "# 生成 K 線圖圖像\n",
        "window_size = 20  # 每張圖顯示20天\n",
        "for i in range(window_size, len(df)):\n",
        "    data_slice = df.iloc[i - window_size:i]\n",
        "    label = df.iloc[i]['Label']\n",
        "    file_path = os.path.join(img_dir, label, f'kline_{i}.png')  # 儲存路徑\n",
        "\n",
        "    # 使用 mplfinance 生成 K 線圖\n",
        "    mpf.plot(data_slice, type='candle', style='charles', savefig=file_path)"
      ],
      "metadata": {
        "id": "y99gJ0qKiyJK"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "import random\n",
        "\n",
        "# 定義來源目錄和目標目錄\n",
        "src_dir = 'kline_images'\n",
        "train_dir = 'kline_images_split/train'\n",
        "val_dir = 'kline_images_split/validation'\n",
        "test_dir = 'kline_images_split/test'\n",
        "\n",
        "# 定義劃分比例\n",
        "train_ratio = 0.7\n",
        "val_ratio = 0.15\n",
        "test_ratio = 0.15\n",
        "\n",
        "# 創建目標目錄\n",
        "for split in ['train', 'validation', 'test']:\n",
        "    for category in ['漲', '跌']:\n",
        "        os.makedirs(os.path.join('kline_images_split', split, category), exist_ok=True)\n",
        "\n",
        "# 開始劃分資料\n",
        "for category in ['漲', '跌']:\n",
        "    files = os.listdir(os.path.join(src_dir, category))\n",
        "    random.shuffle(files)  # 隨機打亂文件順序\n",
        "    total_files = len(files)\n",
        "\n",
        "    # 劃分訓練、驗證和測試集\n",
        "    train_files = files[:int(train_ratio * total_files)]\n",
        "    val_files = files[int(train_ratio * total_files):int((train_ratio + val_ratio) * total_files)]\n",
        "    test_files = files[int((train_ratio + val_ratio) * total_files):]\n",
        "\n",
        "    # 移動文件到對應目錄\n",
        "    for file in train_files:\n",
        "        shutil.move(os.path.join(src_dir, category, file), os.path.join(train_dir, category, file))\n",
        "    for file in val_files:\n",
        "        shutil.move(os.path.join(src_dir, category, file), os.path.join(val_dir, category, file))\n",
        "    for file in test_files:\n",
        "        shutil.move(os.path.join(src_dir, category, file), os.path.join(test_dir, category, file))\n",
        "\n",
        "print(\"資料集劃分完成！\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jrI99X3uvx7N",
        "outputId": "c20a54eb-fa24-4ead-d5b0-5f4ef9aa1e05"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "資料集劃分完成！\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# 訓練集與驗證集的 ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "# 測試集的 ImageDataGenerator\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "# 加載訓練集資料\n",
        "train_data = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=(128, 128),\n",
        "    batch_size=32,\n",
        "    class_mode='binary'\n",
        ")\n",
        "\n",
        "# 加載驗證集資料\n",
        "val_data = val_datagen.flow_from_directory(\n",
        "    val_dir,\n",
        "    target_size=(128, 128),\n",
        "    batch_size=32,\n",
        "    class_mode='binary'\n",
        ")\n",
        "\n",
        "# 加載測試集資料\n",
        "test_data = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=(128, 128),\n",
        "    batch_size=32,\n",
        "    class_mode='binary'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PqY-2YovkdsJ",
        "outputId": "cc510dfb-047b-4be9-afe4-a66d67826871"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 660 images belonging to 2 classes.\n",
            "Found 142 images belonging to 2 classes.\n",
            "Found 142 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 建立 CNN 模型\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "\n",
        "def create_cnn_model(input_shape):\n",
        "    model = models.Sequential([\n",
        "        layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),\n",
        "        layers.MaxPooling2D((2, 2)),\n",
        "        layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "        layers.MaxPooling2D((2, 2)),\n",
        "        layers.Conv2D(128, (3, 3), activation='relu'),\n",
        "        layers.MaxPooling2D((2, 2)),\n",
        "        layers.Flatten(),\n",
        "        layers.Dense(128, activation='relu'),\n",
        "        layers.Dense(1, activation='sigmoid')  # 二分類\n",
        "    ])\n",
        "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "# 建立模型\n",
        "model = create_cnn_model((128, 128, 3))\n",
        "\n",
        "# 4. 訓練模型\n",
        "epochs = 10\n",
        "history = model.fit(train_data, validation_data=val_data, epochs=epochs)\n",
        "\n",
        "# 5. 模型評估\n",
        "loss, accuracy = model.evaluate(val_data)\n",
        "print(f'Validation Loss: {loss}')\n",
        "print(f'Validation Accuracy: {accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjctkUxJmaxZ",
        "outputId": "c2fae225-4be9-4e50-8180-069d5972ec3f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 1s/step - accuracy: 0.4665 - loss: 0.9124 - val_accuracy: 0.7606 - val_loss: 0.6568\n",
            "Epoch 2/10\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 1s/step - accuracy: 0.7279 - loss: 0.6087 - val_accuracy: 0.8169 - val_loss: 0.3625\n",
            "Epoch 3/10\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.8405 - loss: 0.3520 - val_accuracy: 0.9296 - val_loss: 0.2293\n",
            "Epoch 4/10\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 1s/step - accuracy: 0.8877 - loss: 0.2423 - val_accuracy: 0.9155 - val_loss: 0.2381\n",
            "Epoch 5/10\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 1s/step - accuracy: 0.9075 - loss: 0.2096 - val_accuracy: 0.9225 - val_loss: 0.2017\n",
            "Epoch 6/10\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9355 - loss: 0.1518 - val_accuracy: 0.8732 - val_loss: 0.2274\n",
            "Epoch 7/10\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 1s/step - accuracy: 0.9620 - loss: 0.1288 - val_accuracy: 0.8944 - val_loss: 0.2367\n",
            "Epoch 8/10\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 1s/step - accuracy: 0.9658 - loss: 0.0991 - val_accuracy: 0.9296 - val_loss: 0.2287\n",
            "Epoch 9/10\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 1s/step - accuracy: 0.9732 - loss: 0.0753 - val_accuracy: 0.9085 - val_loss: 0.2159\n",
            "Epoch 10/10\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9797 - loss: 0.0567 - val_accuracy: 0.9296 - val_loss: 0.2424\n",
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 376ms/step - accuracy: 0.9283 - loss: 0.2358\n",
            "Validation Loss: 0.24239757657051086\n",
            "Validation Accuracy: 0.9295774698257446\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mplfinance as mpf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 假設你已經用train_data訓練模型，並用test_data進行預測\n",
        "for i in range(20, len(test_data)):\n",
        "    data_slice = test_data.iloc[i - 20:i]  # 取出一段K線圖數據\n",
        "    actual_result = '漲' if test_data.iloc[i]['MA_5'] > test_data.iloc[i]['MA_10'] else '跌'\n",
        "    predicted_result = model.predict(data_slice)  # 使用模型進行預測\n",
        "\n",
        "# 繪製K線圖並疊加實際和預測結果\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "mpf.plot(data_slice, type='candle', ax=ax, style='charles')\n",
        "\n",
        "# 顯示實際結果與預測結果\n",
        "plt.text(0.01, 0.95, f\"實際結果：{actual_result}\", transform=ax.transAxes, fontsize=12, verticalalignment='top')\n",
        "plt.text(0.01, 0.90, f\"預測結果：{predicted_result}\", transform=ax.transAxes, fontsize=12, verticalalignment='top')\n",
        "\n",
        "# 顯示或儲存圖像\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "mdDHlm8NrLWl",
        "outputId": "828296d0-603a-47b1-d766-41a9b51bf7e2"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}