lstm.ipynb

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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU",
    "gpuClass": "standard"
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Gongsoo/Python3/blob/main/lstm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
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      "metadata": {
        "id": "VhuVZYkUzJQv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0d708f8-e574-4607-8438-f940eff96164"
      },
      "source": [
        "#drive mounting\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "3_FkQbZOzVIQ",
        "outputId": "66e539e4-a9cd-4165-f3b1-3759b7ffaf38"
      },
      "source": [
        "#data load\n",
        "import pandas as pd\n",
        "\n",
        "df_cov = pd.read_excel('/content/gdrive/My Drive/data_tx.xlsx')\n",
        "df_cov.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             일일 확진자        누적 확진자     감염재생산지수\n",
              "count    252.000000  2.520000e+02  252.000000\n",
              "mean    4056.888889  3.547426e+05    1.177989\n",
              "std     3460.272018  3.384328e+05    0.469323\n",
              "min        1.000000  3.000000e+00    0.729037\n",
              "25%      972.000000  3.575900e+04    0.925234\n",
              "50%     3605.500000  2.307350e+05    1.085735\n",
              "75%     6302.000000  6.679102e+05    1.195910\n",
              "max    21771.000000  1.022336e+06    3.560410"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>일일 확진자</th>\n",
              "      <th>누적 확진자</th>\n",
              "      <th>감염재생산지수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>252.000000</td>\n",
              "      <td>2.520000e+02</td>\n",
              "      <td>252.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4056.888889</td>\n",
              "      <td>3.547426e+05</td>\n",
              "      <td>1.177989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3460.272018</td>\n",
              "      <td>3.384328e+05</td>\n",
              "      <td>0.469323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>0.729037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>972.000000</td>\n",
              "      <td>3.575900e+04</td>\n",
              "      <td>0.925234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3605.500000</td>\n",
              "      <td>2.307350e+05</td>\n",
              "      <td>1.085735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6302.000000</td>\n",
              "      <td>6.679102e+05</td>\n",
              "      <td>1.195910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21771.000000</td>\n",
              "      <td>1.022336e+06</td>\n",
              "      <td>3.560410</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
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              "      height: 32px;\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b2c24bb8-03f7-44dd-8ae1-e3a3c7ffc043 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b2c24bb8-03f7-44dd-8ae1-e3a3c7ffc043');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "em15b_3AbGcU"
      },
      "source": [
        "# 새 섹션"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsj0emuczYWf"
      },
      "source": [
        "#data making function\n",
        "import numpy as np\n",
        "\n",
        "def monitor_day(x, y, day_size):\n",
        "    x_list = []\n",
        "    y_list = []\n",
        "    for i in range(day_size,len(x)):\n",
        "        x_list.append(np.array(x.iloc[i-day_size:i]))\n",
        "        y_list.append(np.array(y.iloc[i]))\n",
        "    return np.array(x_list), np.array(y_list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3UMuR5LzZ8H"
      },
      "source": [
        "#data making\n",
        "SIZE = 200\n",
        "train = df_cov[:SIZE]\n",
        "test = df_cov[SIZE:]\n",
        "\n",
        "x_cols = ['일일 확진자', '누적 확진자', '감염재생산지수']\n",
        "y_cols = ['일일 확진자']\n",
        "\n",
        "x_data = train[x_cols]\n",
        "y_data = train[y_cols]\n",
        "\n",
        "x_data, y_data = monitor_day(x_data, y_data, 7)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zdYiefizbfC",
        "outputId": "64d2bf4a-0d2f-41e1-ba12-af269a0c19ec"
      },
      "source": [
        "#data split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_valid, y_train, y_valid = train_test_split(x_data, y_data, test_size=0.2)\n",
        "\n",
        "x_train.shape, x_valid.shape\n",
        "y_train.shape, y_valid.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((154, 1), (39, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QXw_IoUzzdF2",
        "outputId": "cfce6835-1bb1-41bc-b76e-b6b9a671ebf9"
      },
      "source": [
        "#lstm model\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, BatchNormalization, Dropout\n",
        "from keras.callbacks import EarlyStopping\n",
        "\n",
        "model = Sequential()\n",
        "model.add(LSTM(128, input_shape=(x_train.shape[1], x_train.shape[2]), activation='relu', return_sequences=False))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(loss='mse', optimizer='adam')\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=32)\n",
        "history = model.fit(x_train, y_train, epochs=200, batch_size=2,validation_data=(x_valid, y_valid), callbacks=early_stop)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "77/77 [==============================] - 4s 8ms/step - loss: 888756544.0000 - val_loss: 666740608.0000\n",
            "Epoch 2/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 899992256.0000 - val_loss: 1047336704.0000\n",
            "Epoch 3/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 897640640.0000 - val_loss: 229852032.0000\n",
            "Epoch 4/200\n",
            "77/77 [==============================] - 1s 10ms/step - loss: 119746760.0000 - val_loss: 93122912.0000\n",
            "Epoch 5/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 101663448.0000 - val_loss: 8956486.0000\n",
            "Epoch 6/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 13707793.0000 - val_loss: 9341262.0000\n",
            "Epoch 7/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 14794496.0000 - val_loss: 18274068.0000\n",
            "Epoch 8/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 37811160.0000 - val_loss: 10522609.0000\n",
            "Epoch 9/200\n",
            "77/77 [==============================] - 1s 7ms/step - loss: 54523144.0000 - val_loss: 9469819.0000\n",
            "Epoch 10/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 16370695.0000 - val_loss: 21769230.0000\n",
            "Epoch 11/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 10295478.0000 - val_loss: 8461612.0000\n",
            "Epoch 12/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 13446793.0000 - val_loss: 5079730.0000\n",
            "Epoch 13/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 15952464.0000 - val_loss: 36815248.0000\n",
            "Epoch 14/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 112286400.0000 - val_loss: 5809711.0000\n",
            "Epoch 15/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 16545970.0000 - val_loss: 3819371.5000\n",
            "Epoch 16/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 20608250.0000 - val_loss: 22920016.0000\n",
            "Epoch 17/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 23778462.0000 - val_loss: 14918498.0000\n",
            "Epoch 18/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 13948330.0000 - val_loss: 13696940.0000\n",
            "Epoch 19/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 298221568.0000 - val_loss: 45961320.0000\n",
            "Epoch 20/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 273871008.0000 - val_loss: 356974528.0000\n",
            "Epoch 21/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 127600616.0000 - val_loss: 19303580.0000\n",
            "Epoch 22/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 72658376.0000 - val_loss: 16536583.0000\n",
            "Epoch 23/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 185373696.0000 - val_loss: 88760120.0000\n",
            "Epoch 24/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 80449080.0000 - val_loss: 109982072.0000\n",
            "Epoch 25/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 109669384.0000 - val_loss: 6891058.0000\n",
            "Epoch 26/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 29044540.0000 - val_loss: 16745825.0000\n",
            "Epoch 27/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 40141944.0000 - val_loss: 136452144.0000\n",
            "Epoch 28/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 23643814.0000 - val_loss: 7098820.0000\n",
            "Epoch 29/200\n",
            "77/77 [==============================] - 1s 7ms/step - loss: 10648647.0000 - val_loss: 7351739.0000\n",
            "Epoch 30/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 18080124.0000 - val_loss: 4912841.0000\n",
            "Epoch 31/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 13435579.0000 - val_loss: 5782849.0000\n",
            "Epoch 32/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 28901502.0000 - val_loss: 2780340.0000\n",
            "Epoch 33/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 11279564.0000 - val_loss: 5148449.0000\n",
            "Epoch 34/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 27390592.0000 - val_loss: 7795765.5000\n",
            "Epoch 35/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 10724847.0000 - val_loss: 4937401.5000\n",
            "Epoch 36/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 22944376.0000 - val_loss: 4704766.0000\n",
            "Epoch 37/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 10894142.0000 - val_loss: 5424081.5000\n",
            "Epoch 38/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 34304700.0000 - val_loss: 43086164.0000\n",
            "Epoch 39/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 18884780.0000 - val_loss: 6719042.5000\n",
            "Epoch 40/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 14454611.0000 - val_loss: 4862573.0000\n",
            "Epoch 41/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 69943432.0000 - val_loss: 59333416.0000\n",
            "Epoch 42/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 44608020.0000 - val_loss: 182000752.0000\n",
            "Epoch 43/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 51172492.0000 - val_loss: 4814248.0000\n",
            "Epoch 44/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 8269137.5000 - val_loss: 5997377.0000\n",
            "Epoch 45/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 7223101.5000 - val_loss: 7001041.0000\n",
            "Epoch 46/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 17353998.0000 - val_loss: 8610903.0000\n",
            "Epoch 47/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 10474950.0000 - val_loss: 5864149.5000\n",
            "Epoch 48/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 198730512.0000 - val_loss: 221349472.0000\n",
            "Epoch 49/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 39843300.0000 - val_loss: 12130576.0000\n",
            "Epoch 50/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 9979183.0000 - val_loss: 6157516.5000\n",
            "Epoch 51/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 8137893.5000 - val_loss: 11213226.0000\n",
            "Epoch 52/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 6437987.0000 - val_loss: 5146209.0000\n",
            "Epoch 53/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 6233036.0000 - val_loss: 3561811.7500\n",
            "Epoch 54/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 7477343.0000 - val_loss: 6236639.0000\n",
            "Epoch 55/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 6289525.0000 - val_loss: 4843536.5000\n",
            "Epoch 56/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 5631922.5000 - val_loss: 4332544.5000\n",
            "Epoch 57/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 7234613.0000 - val_loss: 4429593.5000\n",
            "Epoch 58/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 6460839.5000 - val_loss: 5804645.5000\n",
            "Epoch 59/200\n",
            "77/77 [==============================] - 1s 7ms/step - loss: 6157069.0000 - val_loss: 4754771.5000\n",
            "Epoch 60/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 5551708.0000 - val_loss: 4293516.5000\n",
            "Epoch 61/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 9924538.0000 - val_loss: 4564398.0000\n",
            "Epoch 62/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 3910279.5000 - val_loss: 3672356.5000\n",
            "Epoch 63/200\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 4423586.5000 - val_loss: 3594537.5000\n",
            "Epoch 64/200\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 4310086.5000 - val_loss: 4530731.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Koif0MdKzhin",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "841d9f82-fe99-4248-d447-96f1ebeaa5d1"
      },
      "source": [
        "#model save& model testing\n",
        "from keras.models import load_model\n",
        "model.save('/content/gdrive/My Drive/lstm_tx.h5')\n",
        "x_test = test[x_cols]\n",
        "y_test = test[y_cols]\n",
        "model=load_model('/content/gdrive/My Drive/lstm_tx.h5')\n",
        "x_test, y_test = monitor_day(x_test, y_test, 7)\n",
        "x_test.shape, y_test.shape\n",
        "\n",
        "pred = model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 8ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "aW7C6ap7zyiQ",
        "outputId": "83ab7a38-b194-4f5f-f264-b215c8e47047"
      },
      "source": [
        "#test modeling plot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(12, 9))\n",
        "plt.plot(y_test, label='actual')\n",
        "plt.plot(pred, label='prediction')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UduLPM-uJEWk"
      },
      "source": [
        "#predict function\n",
        "def predict_days(x,y,z) :\n",
        "  temp_arr = np.array([x])\n",
        "  temp_acc = np.array([y+x])\n",
        "  temp_arr = np.append(temp_arr,temp_acc)\n",
        "  temp_rt = np.array([z])\n",
        "  temp_arr= np.append(temp_arr,temp_rt)\n",
        "  return temp_arr"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYWskDKQJElD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0406f52-c49d-4a94-93ec-e83b84ffd791"
      },
      "source": [
        "#predict\n",
        "x_predict=np.zeros((30))\n",
        "x_temp= df_cov[245:]\n",
        "x_temp = x_temp[x_cols]\n",
        "x_temp = np.array(x_temp)\n",
        "x_temp = x_temp.reshape(1,7,3)\n",
        "for i in range(30) :\n",
        "  temp_predict = model.predict(x_temp.reshape(1,7,3))\n",
        "  new_data = predict_days(temp_predict,x_temp[0,6,1],x_temp[0,6,2])\n",
        "  x_predict[i]= temp_predict\n",
        "  x_temp = np.append(x_temp, new_data.reshape(1,1,3), axis=1)\n",
        "  x_temp = np.delete(x_temp,0,axis=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "W7willTFJE0K",
        "outputId": "8390e777-d495-41f6-b216-838b2702b227"
      },
      "source": [
        "#predict plot\n",
        "plt.figure(figsize=(12,9))\n",
        "plt.plot(x_predict, label='predict')\n",
        "plt.legend()\n",
        "plt.show\n",
        "plt.savefig('/content/gdrive/My Drive/test_tx.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmmZGaeMLSh-"
      },
      "source": []
    }
  ]
}