bi_lstmwith_paper_specs.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "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/bi_lstmwith_paper_specs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "kAz8McbzZsV_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4563225-f8da-4312-8574-edcc878c2cc4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "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/drive/MyDrive/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 = 10\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": 955
        },
        "outputId": "6de24ac9-d6dd-47c8-d589-6343b004a719"
      },
      "execution_count": 5,
      "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]\n",
            " [0.13861386 0.17326733 0.17079208 0.13118812 0.12623762 0.16584158\n",
            "  0.11386139 0.11138614 0.12376238 0.17326733]\n",
            " [0.17326733 0.17079208 0.13118812 0.12623762 0.16584158 0.11386139\n",
            "  0.11138614 0.12376238 0.17326733 0.13613861]\n",
            " [0.17079208 0.13118812 0.12623762 0.16584158 0.11386139 0.11138614\n",
            "  0.12376238 0.17326733 0.13613861 0.10643564]\n",
            " [0.13118812 0.12623762 0.16584158 0.11386139 0.11138614 0.12376238\n",
            "  0.17326733 0.13613861 0.10643564 0.0470297 ]]\n",
            "[0.17326733 0.13613861 0.10643564 0.0470297  0.00990099]\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": "7b578cfa-3da1-46b7-a77c-841200bdcad2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(13470, 10, 1)\n",
            "(13470,)\n",
            "(5767, 10, 1)\n",
            "(5767,)\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": 7,
      "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",
        "from tensorflow.keras import layers, regularizers, models\n",
        "\n",
        "# model = models.Sequential([\n",
        "#     layers.Bidirectional(layers.GRU(units=32, activation='relu', return_sequences=True),\n",
        "#                           input_shape=(trainX.shape[1], trainX.shape[2])),\n",
        "#      layers.add(Dropout(0.5))\n",
        "#      layers.add(CNN)\n",
        "#     layers.Dense(units=1, activation='relu', kernel_regularizer=regularizers.l2(0.01))\n",
        "# ])\n",
        "\n",
        "# model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model = Sequential()\n",
        "model.add(Bidirectional(LSTM(30, activation='relu'), input_shape=( look_back,1)))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')\n",
        "# fit model\n",
        "# model.fit(trainX, trainY, epochs=50, batch_size=64, verbose=0, validation_data=(testX, testY))\n",
        "\n",
        "# Fit the model to the training data\n",
        "model.summary()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3DpzHhqeHJJ",
        "outputId": "3a0b770a-3d85-4f55-8382-84abb00c94e6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bidirectional (Bidirectiona  (None, 60)               7680      \n",
            " l)                                                              \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 61        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,741\n",
            "Trainable params: 7,741\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(trainX, trainY, epochs=50, batch_size=64,verbose=1, validation_data=(testX, testY))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fw2o9Mru35eY",
        "outputId": "d79dc90c-3d7e-4d1e-9b4c-58672e16b7cf"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "211/211 [==============================] - 11s 18ms/step - loss: 0.0024 - val_loss: 0.0031\n",
            "Epoch 2/50\n",
            "211/211 [==============================] - 3s 14ms/step - loss: 0.0015 - val_loss: 0.0030\n",
            "Epoch 3/50\n",
            "211/211 [==============================] - 5s 22ms/step - loss: 0.0014 - val_loss: 0.0030\n",
            "Epoch 4/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0014 - val_loss: 0.0030\n",
            "Epoch 5/50\n",
            "211/211 [==============================] - 3s 12ms/step - loss: 0.0014 - val_loss: 0.0028\n",
            "Epoch 6/50\n",
            "211/211 [==============================] - 3s 14ms/step - loss: 0.0014 - val_loss: 0.0028\n",
            "Epoch 7/50\n",
            "211/211 [==============================] - 4s 17ms/step - loss: 0.0014 - val_loss: 0.0028\n",
            "Epoch 8/50\n",
            "211/211 [==============================] - 4s 17ms/step - loss: 0.0014 - val_loss: 0.0029\n",
            "Epoch 9/50\n",
            "211/211 [==============================] - 3s 12ms/step - loss: 0.0014 - val_loss: 0.0028\n",
            "Epoch 10/50\n",
            "211/211 [==============================] - 3s 14ms/step - loss: 0.0014 - val_loss: 0.0027\n",
            "Epoch 11/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0014 - val_loss: 0.0028\n",
            "Epoch 12/50\n",
            "211/211 [==============================] - 4s 20ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 13/50\n",
            "211/211 [==============================] - 3s 15ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 14/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 15/50\n",
            "211/211 [==============================] - 4s 20ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 16/50\n",
            "211/211 [==============================] - 4s 18ms/step - loss: 0.0013 - val_loss: 0.0027\n",
            "Epoch 17/50\n",
            "211/211 [==============================] - 4s 18ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 18/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 19/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 20/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 21/50\n",
            "211/211 [==============================] - 6s 28ms/step - loss: 0.0013 - val_loss: 0.0028\n",
            "Epoch 22/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 23/50\n",
            "211/211 [==============================] - 3s 12ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 24/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 25/50\n",
            "211/211 [==============================] - 4s 17ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 26/50\n",
            "211/211 [==============================] - 4s 19ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 27/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 28/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 29/50\n",
            "211/211 [==============================] - 3s 12ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 30/50\n",
            "211/211 [==============================] - 4s 21ms/step - loss: 0.0013 - val_loss: 0.0027\n",
            "Epoch 31/50\n",
            "211/211 [==============================] - 3s 14ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 32/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 33/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 34/50\n",
            "211/211 [==============================] - 3s 14ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 35/50\n",
            "211/211 [==============================] - 4s 21ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 36/50\n",
            "211/211 [==============================] - 3s 12ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 37/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 38/50\n",
            "211/211 [==============================] - 3s 14ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 39/50\n",
            "211/211 [==============================] - 4s 17ms/step - loss: 0.0013 - val_loss: 0.0027\n",
            "Epoch 40/50\n",
            "211/211 [==============================] - 4s 17ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 41/50\n",
            "211/211 [==============================] - 3s 12ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 42/50\n",
            "211/211 [==============================] - 3s 12ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 43/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 44/50\n",
            "211/211 [==============================] - 4s 18ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 45/50\n",
            "211/211 [==============================] - 3s 16ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 46/50\n",
            "211/211 [==============================] - 3s 12ms/step - loss: 0.0013 - val_loss: 0.0027\n",
            "Epoch 47/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 48/50\n",
            "211/211 [==============================] - 3s 13ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 49/50\n",
            "211/211 [==============================] - 4s 21ms/step - loss: 0.0013 - val_loss: 0.0026\n",
            "Epoch 50/50\n",
            "211/211 [==============================] - 3s 15ms/step - loss: 0.0013 - val_loss: 0.0026\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ffacacd7580>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "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": null,
      "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": null,
      "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": "27f2f036-c4b6-40f1-cfc0-eb519796d5d5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "421/421 [==============================] - 4s 6ms/step\n",
            "181/181 [==============================] - 1s 4ms/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": "b4c35c31-454d-495f-fdcd-0c9bced17490"
      },
      "execution_count": 11,
      "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": "918cafba-22c1-41e9-a0c6-cf5bd8d2864c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Score: 14.64 RMSE\n",
            "Test Score: 20.55 RMSE\n",
            "Time taken for prediction: 1.33 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": "markdown",
      "source": [],
      "metadata": {
        "id": "2GBnn3_Rm2az"
      }
    }
  ]
}