gru32_lstm32.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "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/itsCheithanya/KubeCasten/blob/main/gru32_lstm32.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "DJYX2r2gTKc1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02b2326a-cb94-4b13-de7d-01c47567e1fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  /content/gdrive/My Drive/\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(r'/content/gdrive/My Drive/output1.csv')\n",
        "df.head"
      ],
      "metadata": {
        "id": "nq7XdAXdVPnf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "921f7aff-4db8-4c28-96a8-784b5a01e793"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of                         IP Address                     Date/Time  \\\n",
              "0                     199.72.81.55  [01/Jul/1995:00:00:01 -0400]   \n",
              "1             unicomp6.unicomp.net  [01/Jul/1995:00:00:06 -0400]   \n",
              "2                   199.120.110.21  [01/Jul/1995:00:00:09 -0400]   \n",
              "3               burger.letters.com  [01/Jul/1995:00:00:11 -0400]   \n",
              "4                   199.120.110.21  [01/Jul/1995:00:00:11 -0400]   \n",
              "...                            ...                           ...   \n",
              "1048570  ix-atl10-28.ix.netcom.com  [14/Jul/1995:10:05:59 -0400]   \n",
              "1048571              198.133.29.18  [14/Jul/1995:10:05:59 -0400]   \n",
              "1048572              192.68.108.59  [14/Jul/1995:10:06:00 -0400]   \n",
              "1048573              192.68.108.59  [14/Jul/1995:10:06:00 -0400]   \n",
              "1048574         oregon.oxmol.co.uk  [14/Jul/1995:10:06:00 -0400]   \n",
              "\n",
              "                                                   Request  \n",
              "0                          \"GET /history/apollo/ HTTP/1.0\"  \n",
              "1                       \"GET /shuttle/countdown/ HTTP/1.0\"  \n",
              "2        \"GET /shuttle/missions/sts-73/mission-sts-73.h...  \n",
              "3           \"GET /shuttle/countdown/liftoff.html HTTP/1.0\"  \n",
              "4        \"GET /shuttle/missions/sts-73/sts-73-patch-sma...  \n",
              "...                                                    ...  \n",
              "1048570  \"GET /shuttle/missions/sts-70/movies/movies.ht...  \n",
              "1048571     \"GET /shuttle/missions/missions.html HTTP/1.0\"  \n",
              "1048572           \"GET /images/KSC-logosmall.gif HTTP/1.0\"  \n",
              "1048573          \"GET /images/NASA-logosmall.gif HTTP/1.0\"  \n",
              "1048574                 \"GET /htbin/cdt_clock.pl HTTP/1.0\"  \n",
              "\n",
              "[1048575 rows x 3 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "kAz8McbzZsV_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import array\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dense"
      ],
      "metadata": {
        "id": "d_uBCUZiV5L6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# univariate data preparation\n",
        "from numpy import array\n",
        "\n",
        "# split a univariate sequence into samples\n",
        "def split_sequence(sequence, n_steps):\n",
        "\tX, y = list(), list()\n",
        "\tfor i in range(len(sequence)):\n",
        "\t\t# find the end of this pattern\n",
        "\t\tend_ix = i + n_steps\n",
        "\t\t# check if we are beyond the sequence\n",
        "\t\tif end_ix > len(sequence)-1:\n",
        "\t\t\tbreak\n",
        "\t\t# gather input and output parts of the pattern\n",
        "\t\tseq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
        "\t\tX.append(seq_x)\n",
        "\t\ty.append(seq_y)\n",
        "\treturn array(X), array(y)\n",
        "\n",
        "# define input sequence\n",
        "raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
        "# choose a number of time steps\n",
        "n_steps = 3\n",
        "# split into samples\n",
        "X, y = split_sequence(raw_seq, n_steps)\n",
        "# summarize the data\n",
        "for i in range(len(y)):\n",
        "\tprint(X[i], y[i])\n",
        "print(X.shape)\n",
        "print(X.shape[0])\n",
        "print(X.shape[1])\n",
        "...\n",
        "# reshape from [samples, timesteps] into [samples, timesteps, features]\n",
        "n_features = 1\n",
        "X = X.reshape((X.shape[0], X.shape[1], n_features))\n",
        "# for i in range(len(X)):\n",
        "# \tprint(X[i])\n",
        " \n",
        " \n"
      ],
      "metadata": {
        "id": "kV4SLjn9WM_n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09d55f80-0584-44af-e8c8-dd8f97ff5013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10 20 30] 40\n",
            "[20 30 40] 50\n",
            "[30 40 50] 60\n",
            "[40 50 60] 70\n",
            "[50 60 70] 80\n",
            "[60 70 80] 90\n",
            "(6, 3)\n",
            "6\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lst = [1,2,3,4]\n",
        "lst[0:2]"
      ],
      "metadata": {
        "id": "_N19k1rhvnq0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "415e6af0-5c78-4986-e39b-b615bb189d19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# univariate stacked lstm example\n",
        "from numpy import array\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dense\n",
        "\n",
        "# split a univariate sequence\n",
        "def split_sequence(sequence, n_steps):\n",
        "\tX, y = list(), list()\n",
        "\tfor i in range(len(sequence)):\n",
        "\t\t# find the end of this pattern\n",
        "\t\tend_ix = i + n_steps\n",
        "\t\t# check if we are beyond the sequence\n",
        "\t\tif end_ix > len(sequence)-1:\n",
        "\t\t\tbreak\n",
        "\t\t# gather input and output parts of the pattern\n",
        "\t\tseq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
        "\t\tX.append(seq_x)\n",
        "\t\ty.append(seq_y)\n",
        "\treturn array(X), array(y)\n",
        "\n",
        "# define input sequence\n",
        "raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
        "# choose a number of time steps\n",
        "n_steps = 4\n",
        "# split into samples\n",
        "X, y = split_sequence(raw_seq, n_steps)\n",
        "# reshape from [samples,timesteps] into [samples,timesteps,features]\n",
        "n_features = 1\n",
        "X = X.reshape((X.shape[0], X.shape[1], n_features))\n",
        "# define model\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps, n_features)))\n",
        "model.add(LSTM(50, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')\n",
        "# fit model\n",
        "model.fit(X, y, epochs=200, verbose=0)\n",
        "# demonstrate prediction\n",
        "x_input = array([60,70, 80, 90])\n",
        "x_input = x_input.reshape((1, n_steps, n_features))\n",
        "yhat = model.predict(x_input, verbose=0)\n",
        "print(yhat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2KiYnwsU8YQ",
        "outputId": "4a91c67c-5767-4fee-a919-2f40a727e9b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[103.56283]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head"
      ],
      "metadata": {
        "id": "1QbaBY-EXbyS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e36d6e29-8479-4b54-c143-3accedafe5a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of                         IP Address                     Date/Time  \\\n",
              "0                     199.72.81.55  [01/Jul/1995:00:00:01 -0400]   \n",
              "1             unicomp6.unicomp.net  [01/Jul/1995:00:00:06 -0400]   \n",
              "2                   199.120.110.21  [01/Jul/1995:00:00:09 -0400]   \n",
              "3               burger.letters.com  [01/Jul/1995:00:00:11 -0400]   \n",
              "4                   199.120.110.21  [01/Jul/1995:00:00:11 -0400]   \n",
              "...                            ...                           ...   \n",
              "1048570  ix-atl10-28.ix.netcom.com  [14/Jul/1995:10:05:59 -0400]   \n",
              "1048571              198.133.29.18  [14/Jul/1995:10:05:59 -0400]   \n",
              "1048572              192.68.108.59  [14/Jul/1995:10:06:00 -0400]   \n",
              "1048573              192.68.108.59  [14/Jul/1995:10:06:00 -0400]   \n",
              "1048574         oregon.oxmol.co.uk  [14/Jul/1995:10:06:00 -0400]   \n",
              "\n",
              "                                                   Request  \n",
              "0                          \"GET /history/apollo/ HTTP/1.0\"  \n",
              "1                       \"GET /shuttle/countdown/ HTTP/1.0\"  \n",
              "2        \"GET /shuttle/missions/sts-73/mission-sts-73.h...  \n",
              "3           \"GET /shuttle/countdown/liftoff.html HTTP/1.0\"  \n",
              "4        \"GET /shuttle/missions/sts-73/sts-73-patch-sma...  \n",
              "...                                                    ...  \n",
              "1048570  \"GET /shuttle/missions/sts-70/movies/movies.ht...  \n",
              "1048571     \"GET /shuttle/missions/missions.html HTTP/1.0\"  \n",
              "1048572           \"GET /images/KSC-logosmall.gif HTTP/1.0\"  \n",
              "1048573          \"GET /images/NASA-logosmall.gif HTTP/1.0\"  \n",
              "1048574                 \"GET /htbin/cdt_clock.pl HTTP/1.0\"  \n",
              "\n",
              "[1048575 rows x 3 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas import read_csv\n",
        "import math\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "dataset = pd.read_csv('/content/gdrive/My Drive/output2.csv', usecols=[1], engine='python')\n",
        "plt.plot(dataset)\n",
        "plt.show()\n",
        "# convert an array of values into a dataset matrix\n",
        "def create_dataset(dataset, look_back):\n",
        "\tdataX, dataY = [], []\n",
        "\tfor i in range(len(dataset)-look_back-1):\n",
        "\t\ta = dataset[i:(i+look_back), 0]\n",
        "\t\tdataX.append(a)\n",
        "\t\tdataY.append(dataset[i + look_back, 0])\n",
        "\treturn np.array(dataX), np.array(dataY)\n",
        "\n",
        "# normalize the dataset\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "dataset = scaler.fit_transform(dataset)\n",
        "# split into train and test sets\n",
        "# train_size = int(len(dataset) * 0.63)\n",
        "# val_size=int((len(dataset) - train_size)*0.75)\n",
        "# test_size = (len(dataset) - train_size) - val_size\n",
        "# train, test ,val= dataset[0:train_size,:], dataset[train_size:train_size+val_size,:],dataset[train_size+val_size:len(dataset),:]\n",
        "train_size = int(len(dataset) * 0.70)\n",
        "test_size = len(dataset) - train_size\n",
        "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:] \n",
        "plt.plot(train)\n",
        "plt.show()\n",
        "\n",
        "# plt.plot(val)\n",
        "# plt.show()\n",
        "\n",
        "plt.plot(test)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "# reshape into X=t and Y=t+1\n",
        "look_back = 15\n",
        "trainX, trainY = create_dataset(train, look_back)\n",
        "testX, testY = create_dataset(test, look_back)\n",
        "#valX, valY = create_dataset(val, look_back)\n",
        "print(trainX[:5])\n",
        "print(trainY[:5])\n",
        "#print(valX[:5])\n"
      ],
      "metadata": {
        "id": "aq12uLDNas0m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1ae1108a-1612-4ed6-d025-decf6b8ef985"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.14851485 0.13861386 0.17326733 0.17079208 0.13118812 0.12623762\n",
            "  0.16584158 0.11386139 0.11138614 0.12376238 0.17326733 0.13613861\n",
            "  0.10643564 0.0470297  0.00990099]\n",
            " [0.13861386 0.17326733 0.17079208 0.13118812 0.12623762 0.16584158\n",
            "  0.11386139 0.11138614 0.12376238 0.17326733 0.13613861 0.10643564\n",
            "  0.0470297  0.00990099 0.20049505]\n",
            " [0.17326733 0.17079208 0.13118812 0.12623762 0.16584158 0.11386139\n",
            "  0.11138614 0.12376238 0.17326733 0.13613861 0.10643564 0.0470297\n",
            "  0.00990099 0.20049505 0.12623762]\n",
            " [0.17079208 0.13118812 0.12623762 0.16584158 0.11386139 0.11138614\n",
            "  0.12376238 0.17326733 0.13613861 0.10643564 0.0470297  0.00990099\n",
            "  0.20049505 0.12623762 0.15346535]\n",
            " [0.13118812 0.12623762 0.16584158 0.11386139 0.11138614 0.12376238\n",
            "  0.17326733 0.13613861 0.10643564 0.0470297  0.00990099 0.20049505\n",
            "  0.12623762 0.15346535 0.19554455]]\n",
            "[0.20049505 0.12623762 0.15346535 0.19554455 0.19554455]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# reshape input to be [samples, time steps, features]\n",
        "\n",
        "trainX = trainX.reshape( (trainX.shape[0], trainX.shape[1], 1))\n",
        "testX = testX.reshape( (testX.shape[0],trainX.shape[1], 1))\n",
        "#testY=testY.reshape((trainY.shape[1],trainY.shape[0]))\n",
        "#valX = valX.reshape( (valX.shape[0],valX.shape[1], 1))\n",
        "print(trainX.shape)\n",
        "print(trainY.shape)\n",
        "print(testX.shape)\n",
        "print(testY.shape)\n",
        "#print(valX.shape)"
      ],
      "metadata": {
        "id": "ouoxcdD2kbQU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "761dceb7-be6c-439d-99ae-35a1ffb72db1"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(13465, 15, 1)\n",
            "(13465,)\n",
            "(5762, 15, 1)\n",
            "(5762,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ZmRPTJfnka-2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# univariate bidirectional lstm example\n",
        "from numpy import array\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM,GRU\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Bidirectional\n",
        "from keras.layers import *\n",
        "\n"
      ],
      "metadata": {
        "id": "bhKcaVRaNMrB"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Mo8KZc9Zf14r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define model\n",
        "# create and fit the LSTM network\n",
        "# model = Sequential()\n",
        "# model.add(Bidirectional(LSTM(64, activation='relu'), input_shape=(trainX.shape[1],trainX.shape[2] )))\n",
        "# model.add(Dense(1))\n",
        "# model.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "# model.summary()\n",
        "\n",
        "# model2 = Sequential()\n",
        "# model2.add(Bidirectional(GRU(64, activation='relu'), input_shape=(trainX.shape[1], trainX.shape[2])))\n",
        "# model2.add(Dropout(0.5))\n",
        "# model2.add(Dense(1))\n",
        "\n",
        "# model2.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "# model2.summary()\n",
        "\n",
        "\n",
        "# model4 = Sequential()\n",
        "# model4.add(InputLayer((trainX.shape[1],trainX.shape[2])))\n",
        "# model4.add(GRU(64))\n",
        "# model4.add(Dense(8, 'relu'))\n",
        "# model4.add(Dense(1, 'linear'))\n",
        "# model4.compile(optimizer='adam',loss='mse')\n",
        "\n",
        "# model4.summary()\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import InputLayer, GRU, Dense, Dropout\n",
        "from tensorflow.keras.regularizers import l2\n",
        "# from tensorflow.keras.optimizers import SGD\n",
        "\n",
        "# Define the model architecture\n",
        "model = Sequential()\n",
        "model.add(InputLayer(input_shape=(trainX.shape[1], trainX.shape[2])))\n",
        "model.add(GRU(32, return_sequences=True))\n",
        "model.add(LSTM(32))\n",
        "model.add(Dense(8, activation='linear'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(1, activation='linear', kernel_regularizer=l2(0.01)))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "\n",
        "# Fit the model to the training data\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3DpzHhqeHJJ",
        "outputId": "95618e4b-4572-4479-d4f9-8e595267939f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " gru_14 (GRU)                (None, 15, 32)            3360      \n",
            "                                                                 \n",
            " lstm_13 (LSTM)              (None, 32)                8320      \n",
            "                                                                 \n",
            " dense_25 (Dense)            (None, 8)                 264       \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 8)                 0         \n",
            "                                                                 \n",
            " dense_26 (Dense)            (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11,953\n",
            "Trainable params: 11,953\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(trainX, trainY, epochs=25, batch_size=20, validation_data=(testX, testY))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fw2o9Mru35eY",
        "outputId": "39dc9eb4-e12b-4b02-a5da-41c0755e23f1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "674/674 [==============================] - 18s 27ms/step - loss: 0.0044 - val_loss: 0.0035\n",
            "Epoch 2/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0018 - val_loss: 0.0035\n",
            "Epoch 3/25\n",
            "674/674 [==============================] - 17s 25ms/step - loss: 0.0017 - val_loss: 0.0042\n",
            "Epoch 4/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0016 - val_loss: 0.0030\n",
            "Epoch 5/25\n",
            "674/674 [==============================] - 17s 25ms/step - loss: 0.0016 - val_loss: 0.0029\n",
            "Epoch 6/25\n",
            "674/674 [==============================] - 19s 28ms/step - loss: 0.0016 - val_loss: 0.0033\n",
            "Epoch 7/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0042\n",
            "Epoch 8/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0031\n",
            "Epoch 9/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0035\n",
            "Epoch 10/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0035\n",
            "Epoch 11/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0032\n",
            "Epoch 12/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0032\n",
            "Epoch 13/25\n",
            "674/674 [==============================] - 17s 25ms/step - loss: 0.0015 - val_loss: 0.0034\n",
            "Epoch 14/25\n",
            "674/674 [==============================] - 17s 25ms/step - loss: 0.0015 - val_loss: 0.0039\n",
            "Epoch 15/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0039\n",
            "Epoch 16/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0033\n",
            "Epoch 17/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0033\n",
            "Epoch 18/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0035\n",
            "Epoch 19/25\n",
            "674/674 [==============================] - 18s 26ms/step - loss: 0.0015 - val_loss: 0.0036\n",
            "Epoch 20/25\n",
            "674/674 [==============================] - 17s 25ms/step - loss: 0.0015 - val_loss: 0.0036\n",
            "Epoch 21/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0033\n",
            "Epoch 22/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0033\n",
            "Epoch 23/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0031\n",
            "Epoch 24/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0036\n",
            "Epoch 25/25\n",
            "674/674 [==============================] - 16s 24ms/step - loss: 0.0015 - val_loss: 0.0034\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f214d7fdee0>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit model\n",
        "model2.fit(trainX, trainY, epochs=10, batch_size=10, verbose=0)\n",
        "\n",
        "# from keras.callbacks import EarlyStopping\n",
        "\n",
        "# early_stop = EarlyStopping(monitor='val_loss', patience=10)\n",
        "\n",
        "# model2.fit( trainX, trainY,validation_data=(valX, valY), epochs=15, callbacks=[early_stop])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "SzW11oUdZ2FM",
        "outputId": "1b4b7be1-a816-48b2-b0e7-6f444418cc95"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-106e869be621>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# fit model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# from keras.callbacks import EarlyStopping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1648\u001b[0m                         ):\n\u001b[1;32m   1649\u001b[0m                             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1650\u001b[0;31m                             \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1651\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1652\u001b[0m                                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    878\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 880\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    910\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_variable_creation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    132\u001b[0m       (concrete_function,\n\u001b[1;32m    133\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m--> 134\u001b[0;31m     return concrete_function._call_flat(\n\u001b[0m\u001b[1;32m    135\u001b[0m         filtered_flat_args, captured_inputs=concrete_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1743\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1745\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1746\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    376\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    379\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit model\n",
        "model4.fit(trainX, trainY, epochs=10, batch_size=10, verbose=0)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhmpxPcuBHzt",
        "outputId": "207ddffe-c74e-4a31-c39f-78796a795e81"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f214e7bb790>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make predictions\n",
        "import time\n",
        "trainPredict = model.predict(trainX)\n",
        "start_time = time.time()\n",
        "testPredict = model.predict(testX)\n",
        "end_time = time.time()\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])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYAb-dIVfS-S",
        "outputId": "50c4c8b0-32a9-44c2-baf4-6289fe9119b6"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "421/421 [==============================] - 4s 6ms/step\n",
            "181/181 [==============================] - 1s 6ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "plt.plot(testPredict)\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "aDK13fnqohPg",
        "outputId": "de1d3e08-d735-4707-d9ac-b87a7ff8d89d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\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))\n",
        "\n",
        "\n",
        "\n",
        "print('Time taken for prediction: {:.2f} seconds'.format(end_time - start_time))\n",
        "\n",
        "# shift train predictions for plotting\n",
        "trainPredictPlot = np.empty_like(dataset)\n",
        "trainPredictPlot[:, :] = np.nan\n",
        "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
        "# shift test predictions for plotting\n",
        "testPredictPlot = np.empty_like(dataset)\n",
        "testPredictPlot[:, :] = np.nan\n",
        "testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n",
        "# plot baseline and predictions\n",
        "plt.plot(scaler.inverse_transform(dataset))\n",
        "plt.plot(trainPredictPlot)\n",
        "plt.plot(testPredictPlot)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "iTt7B9Wpo5kL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "30c1cb90-69be-4a25-c41d-b60a1f2d5231"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Score: 14.95 RMSE\n",
            "Test Score: 23.52 RMSE\n",
            "Time taken for prediction: 1.23 seconds\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('/content/gdrive/My Drive/modelgru&lstm.h5')"
      ],
      "metadata": {
        "id": "eooQZmLsCzlo"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_weights('path_to_saved_weights.h5')"
      ],
      "metadata": {
        "id": "besjMcf_FKPC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "StmpwE0VFK3j"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}