LSTM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOh3qmnsUAj+ykTnshysjmf",
      "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/justSammy1604/Python-Learning-Stuff/blob/main/LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "Uwg_1NRYYdsx"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/airline_passenger.csv',usecols=[1],engine='python')\n",
        "ds = df.values\n",
        "ds = ds.astype('float32')\n",
        "plt.plot(df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "W9Ih-RcOcSBv",
        "outputId": "10db4338-ab87-4bd7-82e4-db2052d520b5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "ds = scaler.fit_transform(ds)"
      ],
      "metadata": {
        "id": "IeBeinX1cpE6"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_size = int(len(ds)*0.69)\n",
        "test_size = len(ds) - train_size\n",
        "train,test = ds[0:train_size,:],ds[train_size:len(ds),:]\n",
        "print(len(train),len(test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1o-aw0zdVHa",
        "outputId": "84d09c9d-85d6-4e96-d16f-7c3d3b6dd67b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99 45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_ds(ds,look_back=1):\n",
        "  dX,dY = [], []\n",
        "  for i in range(len(ds)-look_back-1):\n",
        "    a = ds[i:(i+look_back),0]\n",
        "    dX.append(a)\n",
        "    dY.append(ds[i+look_back,0])\n",
        "\n",
        "  return np.array(dX),np.array(dY)"
      ],
      "metadata": {
        "id": "UuNq0nfse853"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape into X=t and Y=t+1\n",
        "look_back = 1\n",
        "trainX, trainY = create_ds(train, look_back)\n",
        "testX, testY = create_ds(test, look_back)\n",
        "\n",
        "# reshape input to be [samples, time steps, features]\n",
        "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
        "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
      ],
      "metadata": {
        "id": "1deNPAGtfvWY"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(4,input_shape=(1,look_back)))\n",
        "model.add(Dense(3))\n",
        "model.compile(loss='mean_squared_error',optimizer='adam',metrics=['accuracy','mse','mae'])\n",
        "model.fit(trainX,trainY,epochs=100,batch_size=5,verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XulkHFLqfzmt",
        "outputId": "5c46cfdd-0196-40dc-abe1-a7bca33653db"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "20/20 - 3s - loss: 0.0493 - accuracy: 0.0000e+00 - mse: 0.0493 - mae: 0.1831 - 3s/epoch - 156ms/step\n",
            "Epoch 2/100\n",
            "20/20 - 0s - loss: 0.0401 - accuracy: 0.0000e+00 - mse: 0.0401 - mae: 0.1584 - 50ms/epoch - 3ms/step\n",
            "Epoch 3/100\n",
            "20/20 - 0s - loss: 0.0322 - accuracy: 0.0000e+00 - mse: 0.0322 - mae: 0.1379 - 51ms/epoch - 3ms/step\n",
            "Epoch 4/100\n",
            "20/20 - 0s - loss: 0.0263 - accuracy: 0.0000e+00 - mse: 0.0263 - mae: 0.1232 - 51ms/epoch - 3ms/step\n",
            "Epoch 5/100\n",
            "20/20 - 0s - loss: 0.0220 - accuracy: 0.0000e+00 - mse: 0.0220 - mae: 0.1116 - 52ms/epoch - 3ms/step\n",
            "Epoch 6/100\n",
            "20/20 - 0s - loss: 0.0189 - accuracy: 0.0000e+00 - mse: 0.0189 - mae: 0.1050 - 52ms/epoch - 3ms/step\n",
            "Epoch 7/100\n",
            "20/20 - 0s - loss: 0.0166 - accuracy: 0.0000e+00 - mse: 0.0166 - mae: 0.0991 - 53ms/epoch - 3ms/step\n",
            "Epoch 8/100\n",
            "20/20 - 0s - loss: 0.0150 - accuracy: 0.0000e+00 - mse: 0.0150 - mae: 0.0958 - 51ms/epoch - 3ms/step\n",
            "Epoch 9/100\n",
            "20/20 - 0s - loss: 0.0139 - accuracy: 0.0000e+00 - mse: 0.0139 - mae: 0.0938 - 58ms/epoch - 3ms/step\n",
            "Epoch 10/100\n",
            "20/20 - 0s - loss: 0.0131 - accuracy: 0.0000e+00 - mse: 0.0131 - mae: 0.0917 - 51ms/epoch - 3ms/step\n",
            "Epoch 11/100\n",
            "20/20 - 0s - loss: 0.0125 - accuracy: 0.0000e+00 - mse: 0.0125 - mae: 0.0903 - 50ms/epoch - 3ms/step\n",
            "Epoch 12/100\n",
            "20/20 - 0s - loss: 0.0119 - accuracy: 0.0000e+00 - mse: 0.0119 - mae: 0.0890 - 51ms/epoch - 3ms/step\n",
            "Epoch 13/100\n",
            "20/20 - 0s - loss: 0.0116 - accuracy: 0.0103 - mse: 0.0116 - mae: 0.0880 - 50ms/epoch - 2ms/step\n",
            "Epoch 14/100\n",
            "20/20 - 0s - loss: 0.0111 - accuracy: 0.0103 - mse: 0.0111 - mae: 0.0866 - 50ms/epoch - 3ms/step\n",
            "Epoch 15/100\n",
            "20/20 - 0s - loss: 0.0109 - accuracy: 0.0103 - mse: 0.0109 - mae: 0.0856 - 50ms/epoch - 2ms/step\n",
            "Epoch 16/100\n",
            "20/20 - 0s - loss: 0.0105 - accuracy: 0.0103 - mse: 0.0105 - mae: 0.0840 - 61ms/epoch - 3ms/step\n",
            "Epoch 17/100\n",
            "20/20 - 0s - loss: 0.0101 - accuracy: 0.0103 - mse: 0.0101 - mae: 0.0824 - 53ms/epoch - 3ms/step\n",
            "Epoch 18/100\n",
            "20/20 - 0s - loss: 0.0098 - accuracy: 0.0103 - mse: 0.0098 - mae: 0.0808 - 60ms/epoch - 3ms/step\n",
            "Epoch 19/100\n",
            "20/20 - 0s - loss: 0.0094 - accuracy: 0.0103 - mse: 0.0094 - mae: 0.0792 - 53ms/epoch - 3ms/step\n",
            "Epoch 20/100\n",
            "20/20 - 0s - loss: 0.0091 - accuracy: 0.0103 - mse: 0.0091 - mae: 0.0779 - 54ms/epoch - 3ms/step\n",
            "Epoch 21/100\n",
            "20/20 - 0s - loss: 0.0088 - accuracy: 0.0103 - mse: 0.0088 - mae: 0.0766 - 54ms/epoch - 3ms/step\n",
            "Epoch 22/100\n",
            "20/20 - 0s - loss: 0.0084 - accuracy: 0.0103 - mse: 0.0084 - mae: 0.0750 - 53ms/epoch - 3ms/step\n",
            "Epoch 23/100\n",
            "20/20 - 0s - loss: 0.0081 - accuracy: 0.0103 - mse: 0.0081 - mae: 0.0730 - 51ms/epoch - 3ms/step\n",
            "Epoch 24/100\n",
            "20/20 - 0s - loss: 0.0078 - accuracy: 0.0103 - mse: 0.0078 - mae: 0.0716 - 52ms/epoch - 3ms/step\n",
            "Epoch 25/100\n",
            "20/20 - 0s - loss: 0.0074 - accuracy: 0.0103 - mse: 0.0074 - mae: 0.0698 - 53ms/epoch - 3ms/step\n",
            "Epoch 26/100\n",
            "20/20 - 0s - loss: 0.0071 - accuracy: 0.0103 - mse: 0.0071 - mae: 0.0678 - 63ms/epoch - 3ms/step\n",
            "Epoch 27/100\n",
            "20/20 - 0s - loss: 0.0067 - accuracy: 0.0103 - mse: 0.0067 - mae: 0.0662 - 54ms/epoch - 3ms/step\n",
            "Epoch 28/100\n",
            "20/20 - 0s - loss: 0.0064 - accuracy: 0.0103 - mse: 0.0064 - mae: 0.0645 - 52ms/epoch - 3ms/step\n",
            "Epoch 29/100\n",
            "20/20 - 0s - loss: 0.0061 - accuracy: 0.0103 - mse: 0.0061 - mae: 0.0629 - 54ms/epoch - 3ms/step\n",
            "Epoch 30/100\n",
            "20/20 - 0s - loss: 0.0058 - accuracy: 0.0103 - mse: 0.0058 - mae: 0.0610 - 55ms/epoch - 3ms/step\n",
            "Epoch 31/100\n",
            "20/20 - 0s - loss: 0.0055 - accuracy: 0.0103 - mse: 0.0055 - mae: 0.0595 - 51ms/epoch - 3ms/step\n",
            "Epoch 32/100\n",
            "20/20 - 0s - loss: 0.0052 - accuracy: 0.0103 - mse: 0.0052 - mae: 0.0578 - 51ms/epoch - 3ms/step\n",
            "Epoch 33/100\n",
            "20/20 - 0s - loss: 0.0050 - accuracy: 0.0103 - mse: 0.0050 - mae: 0.0562 - 51ms/epoch - 3ms/step\n",
            "Epoch 34/100\n",
            "20/20 - 0s - loss: 0.0047 - accuracy: 0.0103 - mse: 0.0047 - mae: 0.0544 - 51ms/epoch - 3ms/step\n",
            "Epoch 35/100\n",
            "20/20 - 0s - loss: 0.0045 - accuracy: 0.0103 - mse: 0.0045 - mae: 0.0532 - 50ms/epoch - 2ms/step\n",
            "Epoch 36/100\n",
            "20/20 - 0s - loss: 0.0043 - accuracy: 0.0103 - mse: 0.0043 - mae: 0.0524 - 50ms/epoch - 3ms/step\n",
            "Epoch 37/100\n",
            "20/20 - 0s - loss: 0.0041 - accuracy: 0.0103 - mse: 0.0041 - mae: 0.0507 - 52ms/epoch - 3ms/step\n",
            "Epoch 38/100\n",
            "20/20 - 0s - loss: 0.0039 - accuracy: 0.0103 - mse: 0.0039 - mae: 0.0493 - 52ms/epoch - 3ms/step\n",
            "Epoch 39/100\n",
            "20/20 - 0s - loss: 0.0037 - accuracy: 0.0103 - mse: 0.0037 - mae: 0.0483 - 53ms/epoch - 3ms/step\n",
            "Epoch 40/100\n",
            "20/20 - 0s - loss: 0.0036 - accuracy: 0.0103 - mse: 0.0036 - mae: 0.0474 - 53ms/epoch - 3ms/step\n",
            "Epoch 41/100\n",
            "20/20 - 0s - loss: 0.0034 - accuracy: 0.0103 - mse: 0.0034 - mae: 0.0466 - 56ms/epoch - 3ms/step\n",
            "Epoch 42/100\n",
            "20/20 - 0s - loss: 0.0033 - accuracy: 0.0103 - mse: 0.0033 - mae: 0.0456 - 52ms/epoch - 3ms/step\n",
            "Epoch 43/100\n",
            "20/20 - 0s - loss: 0.0031 - accuracy: 0.0103 - mse: 0.0031 - mae: 0.0446 - 60ms/epoch - 3ms/step\n",
            "Epoch 44/100\n",
            "20/20 - 0s - loss: 0.0030 - accuracy: 0.0103 - mse: 0.0030 - mae: 0.0439 - 56ms/epoch - 3ms/step\n",
            "Epoch 45/100\n",
            "20/20 - 0s - loss: 0.0029 - accuracy: 0.0103 - mse: 0.0029 - mae: 0.0433 - 51ms/epoch - 3ms/step\n",
            "Epoch 46/100\n",
            "20/20 - 0s - loss: 0.0028 - accuracy: 0.0103 - mse: 0.0028 - mae: 0.0426 - 51ms/epoch - 3ms/step\n",
            "Epoch 47/100\n",
            "20/20 - 0s - loss: 0.0028 - accuracy: 0.0103 - mse: 0.0028 - mae: 0.0419 - 50ms/epoch - 2ms/step\n",
            "Epoch 48/100\n",
            "20/20 - 0s - loss: 0.0027 - accuracy: 0.0103 - mse: 0.0027 - mae: 0.0414 - 51ms/epoch - 3ms/step\n",
            "Epoch 49/100\n",
            "20/20 - 0s - loss: 0.0026 - accuracy: 0.0103 - mse: 0.0026 - mae: 0.0409 - 50ms/epoch - 3ms/step\n",
            "Epoch 50/100\n",
            "20/20 - 0s - loss: 0.0026 - accuracy: 0.0103 - mse: 0.0026 - mae: 0.0407 - 53ms/epoch - 3ms/step\n",
            "Epoch 51/100\n",
            "20/20 - 0s - loss: 0.0025 - accuracy: 0.0103 - mse: 0.0025 - mae: 0.0400 - 59ms/epoch - 3ms/step\n",
            "Epoch 52/100\n",
            "20/20 - 0s - loss: 0.0024 - accuracy: 0.0103 - mse: 0.0024 - mae: 0.0396 - 51ms/epoch - 3ms/step\n",
            "Epoch 53/100\n",
            "20/20 - 0s - loss: 0.0024 - accuracy: 0.0103 - mse: 0.0024 - mae: 0.0392 - 51ms/epoch - 3ms/step\n",
            "Epoch 54/100\n",
            "20/20 - 0s - loss: 0.0024 - accuracy: 0.0103 - mse: 0.0024 - mae: 0.0389 - 52ms/epoch - 3ms/step\n",
            "Epoch 55/100\n",
            "20/20 - 0s - loss: 0.0023 - accuracy: 0.0103 - mse: 0.0023 - mae: 0.0384 - 53ms/epoch - 3ms/step\n",
            "Epoch 56/100\n",
            "20/20 - 0s - loss: 0.0023 - accuracy: 0.0103 - mse: 0.0023 - mae: 0.0382 - 54ms/epoch - 3ms/step\n",
            "Epoch 57/100\n",
            "20/20 - 0s - loss: 0.0022 - accuracy: 0.0103 - mse: 0.0022 - mae: 0.0377 - 52ms/epoch - 3ms/step\n",
            "Epoch 58/100\n",
            "20/20 - 0s - loss: 0.0022 - accuracy: 0.0103 - mse: 0.0022 - mae: 0.0375 - 52ms/epoch - 3ms/step\n",
            "Epoch 59/100\n",
            "20/20 - 0s - loss: 0.0022 - accuracy: 0.0103 - mse: 0.0022 - mae: 0.0374 - 51ms/epoch - 3ms/step\n",
            "Epoch 60/100\n",
            "20/20 - 0s - loss: 0.0022 - accuracy: 0.0103 - mse: 0.0022 - mae: 0.0372 - 53ms/epoch - 3ms/step\n",
            "Epoch 61/100\n",
            "20/20 - 0s - loss: 0.0021 - accuracy: 0.0103 - mse: 0.0021 - mae: 0.0370 - 68ms/epoch - 3ms/step\n",
            "Epoch 62/100\n",
            "20/20 - 0s - loss: 0.0021 - accuracy: 0.0103 - mse: 0.0021 - mae: 0.0367 - 51ms/epoch - 3ms/step\n",
            "Epoch 63/100\n",
            "20/20 - 0s - loss: 0.0021 - accuracy: 0.0103 - mse: 0.0021 - mae: 0.0368 - 52ms/epoch - 3ms/step\n",
            "Epoch 64/100\n",
            "20/20 - 0s - loss: 0.0021 - accuracy: 0.0103 - mse: 0.0021 - mae: 0.0363 - 51ms/epoch - 3ms/step\n",
            "Epoch 65/100\n",
            "20/20 - 0s - loss: 0.0021 - accuracy: 0.0103 - mse: 0.0021 - mae: 0.0363 - 52ms/epoch - 3ms/step\n",
            "Epoch 66/100\n",
            "20/20 - 0s - loss: 0.0021 - accuracy: 0.0103 - mse: 0.0021 - mae: 0.0363 - 52ms/epoch - 3ms/step\n",
            "Epoch 67/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0360 - 53ms/epoch - 3ms/step\n",
            "Epoch 68/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0358 - 51ms/epoch - 3ms/step\n",
            "Epoch 69/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0357 - 51ms/epoch - 3ms/step\n",
            "Epoch 70/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0358 - 54ms/epoch - 3ms/step\n",
            "Epoch 71/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0356 - 51ms/epoch - 3ms/step\n",
            "Epoch 72/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0354 - 54ms/epoch - 3ms/step\n",
            "Epoch 73/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0355 - 52ms/epoch - 3ms/step\n",
            "Epoch 74/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0353 - 51ms/epoch - 3ms/step\n",
            "Epoch 75/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0352 - 53ms/epoch - 3ms/step\n",
            "Epoch 76/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0354 - 52ms/epoch - 3ms/step\n",
            "Epoch 77/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0351 - 53ms/epoch - 3ms/step\n",
            "Epoch 78/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0350 - 74ms/epoch - 4ms/step\n",
            "Epoch 79/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0355 - 53ms/epoch - 3ms/step\n",
            "Epoch 80/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0352 - 57ms/epoch - 3ms/step\n",
            "Epoch 81/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0349 - 53ms/epoch - 3ms/step\n",
            "Epoch 82/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0350 - 53ms/epoch - 3ms/step\n",
            "Epoch 83/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0349 - 53ms/epoch - 3ms/step\n",
            "Epoch 84/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0349 - 54ms/epoch - 3ms/step\n",
            "Epoch 85/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0350 - 53ms/epoch - 3ms/step\n",
            "Epoch 86/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0349 - 53ms/epoch - 3ms/step\n",
            "Epoch 87/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0353 - 53ms/epoch - 3ms/step\n",
            "Epoch 88/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0348 - 61ms/epoch - 3ms/step\n",
            "Epoch 89/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0348 - 52ms/epoch - 3ms/step\n",
            "Epoch 90/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0350 - 51ms/epoch - 3ms/step\n",
            "Epoch 91/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0348 - 55ms/epoch - 3ms/step\n",
            "Epoch 92/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0349 - 53ms/epoch - 3ms/step\n",
            "Epoch 93/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0347 - 52ms/epoch - 3ms/step\n",
            "Epoch 94/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0347 - 51ms/epoch - 3ms/step\n",
            "Epoch 95/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0350 - 65ms/epoch - 3ms/step\n",
            "Epoch 96/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0348 - 52ms/epoch - 3ms/step\n",
            "Epoch 97/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0348 - 51ms/epoch - 3ms/step\n",
            "Epoch 98/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0347 - 52ms/epoch - 3ms/step\n",
            "Epoch 99/100\n",
            "20/20 - 0s - loss: 0.0019 - accuracy: 0.0103 - mse: 0.0019 - mae: 0.0347 - 55ms/epoch - 3ms/step\n",
            "Epoch 100/100\n",
            "20/20 - 0s - loss: 0.0020 - accuracy: 0.0103 - mse: 0.0020 - mae: 0.0352 - 53ms/epoch - 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x780538e256f0>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make predictions\n",
        "trainPredict = model.predict(trainX)\n",
        "testPredict = model.predict(testX)\n",
        "# invert predictions\n",
        "trainPredict = scaler.inverse_transform(trainPredict)\n",
        "trainY = scaler.inverse_transform([trainY])\n",
        "testPredict = scaler.inverse_transform(testPredict)\n",
        "testY = scaler.inverse_transform([testY])\n",
        "# calculate root mean squared error\n",
        "trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\n",
        "print('Train Score: %.2f RMSE' % (trainScore))\n",
        "testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\n",
        "print('Test Score: %.2f RMSE' % (testScore))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3U1-kJUkh3Uz",
        "outputId": "dd1a4b9e-aadb-442e-e7b3-b31f0be4632d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n",
            "2/2 [==============================] - 0s 4ms/step\n",
            "Train Score: 0.04 RMSE\n",
            "Test Score: 0.10 RMSE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# shift train predictions for plotting\n",
        "trainPredictPlot = np.empty_like(ds)\n",
        "trainPredictPlot[:, :] = np.nan\n",
        "trainPredictPlot[look_back:len(trainPredict)+look_back, 0] = trainPredict[:, 0]\n",
        "# shift test predictions for plotting\n",
        "testPredictPlot = np.empty_like(ds)\n",
        "testPredictPlot[:, :] = np.nan\n",
        "testPredictPlot[len(trainPredict)+(look_back*2)+1:len(ds)-1, 0] = testPredict[:, 0]\n",
        "# plot baseline and predictions\n",
        "plt.plot(scaler.inverse_transform(ds))\n",
        "plt.plot(trainPredictPlot)\n",
        "plt.plot(testPredictPlot)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "idfmBRG5h7DH",
        "outputId": "b9304e45-506d-47c3-bc70-05c61d94e022"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(trainPredict.shape)\n",
        "print(trainPredictPlot.shape\n",
        "      )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1VKQLgOj69U",
        "outputId": "e4c1e0dd-8e1e-4b75-d415-b9a9705d1008"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(97, 3)\n",
            "(144, 1)\n"
          ]
        }
      ]
    }
  ]
}