Project_3_Prediction_using_RNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOwlx4mBVJdY01zJEPsGkTP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aaina26/Deep-Learning-Unschool/blob/main/Project_3_Prediction_using_RNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "G5XOYtoK8vNO"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Input, SimpleRNN, Dense, Flatten\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "CPMarrzKDakO"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create original data\n",
        "series=np.cos((0.1*np.arange(500))**2)#wave of the form cos(wt2)\n"
      ],
      "metadata": {
        "id": "dVRrn08zEX9q"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot the wave\n",
        "plt.plot(series,'green')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "0NpSqzWZGkLC",
        "outputId": "55531bd6-51f6-467f-8b14-21e70f4799b3"
      },
      "execution_count": 4,
      "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": [
        "T=10 #time step..number of previous values that will be used to predict the output\n",
        "D=1 #dimension or number of features=1\n",
        "X=[] #input\n",
        "Y=[] #output\n",
        "for t in range(len(series)-T):\n",
        "  x=series[t:t+T]\n",
        "  X.append(x)\n",
        "  y=series[t+T]\n",
        "  Y.append(y)\n",
        "\n",
        "X=np.array(X)  #.reshape(-1,T)#there was no need to reshape but the commented code can be used if reshape required #converting appended lists to numpy arrays\n",
        "Y=np.array(Y)\n",
        "print(\"X shape\",X.shape,\"Y shape\", Y.shape)"
      ],
      "metadata": {
        "id": "8OQFwCJkG36K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "662df660-27d4-4d0a-cab3-a208bad4b50e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X shape (490, 10) Y shape (490,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RNN model\n",
        "#reshape x to N*T*D where N is number of inputs and Ti stime step and D is dimension\n",
        "X=X.reshape(-1,T,1)\n",
        "\n",
        "#make RNN\n",
        "i=Input(shape=(T,D))#input layer i is created\n",
        "x=SimpleRNN(10)(i) #10 is number of hidden layers\n",
        "x=Dense(1)(x) #output layer\n",
        "model=Model(i,x)\n",
        "model.compile(\n",
        "    loss='mse',\n",
        "    optimizer=Adam(lr=0.05),\n",
        ")\n",
        "\n",
        "\n",
        "#model train\n",
        "N=490\n",
        "r=model.fit(\n",
        "    X[:-N//2],Y[:-N//2],\n",
        "    batch_size=32,\n",
        "    epochs=200,\n",
        "    validation_data=(X[-N//2:], Y[-N//2:]),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FUc7ONQPvNUf",
        "outputId": "4888bd55-b85c-4fff-f5ae-5697f2949fe0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "8/8 [==============================] - 6s 49ms/step - loss: 0.5045 - val_loss: 0.7289\n",
            "Epoch 2/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.4721 - val_loss: 0.7264\n",
            "Epoch 3/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.4497 - val_loss: 0.7202\n",
            "Epoch 4/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.4353 - val_loss: 0.7183\n",
            "Epoch 5/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.4196 - val_loss: 0.7164\n",
            "Epoch 6/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.4101 - val_loss: 0.7120\n",
            "Epoch 7/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.4022 - val_loss: 0.7087\n",
            "Epoch 8/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3969 - val_loss: 0.7080\n",
            "Epoch 9/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.3914 - val_loss: 0.7035\n",
            "Epoch 10/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3865 - val_loss: 0.7003\n",
            "Epoch 11/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.3825 - val_loss: 0.6941\n",
            "Epoch 12/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.3794 - val_loss: 0.6901\n",
            "Epoch 13/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3757 - val_loss: 0.6848\n",
            "Epoch 14/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.3719 - val_loss: 0.6779\n",
            "Epoch 15/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.3691 - val_loss: 0.6762\n",
            "Epoch 16/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.3662 - val_loss: 0.6717\n",
            "Epoch 17/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.3631 - val_loss: 0.6698\n",
            "Epoch 18/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.3604 - val_loss: 0.6619\n",
            "Epoch 19/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.3574 - val_loss: 0.6637\n",
            "Epoch 20/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3549 - val_loss: 0.6591\n",
            "Epoch 21/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3520 - val_loss: 0.6593\n",
            "Epoch 22/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.3493 - val_loss: 0.6559\n",
            "Epoch 23/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.3470 - val_loss: 0.6553\n",
            "Epoch 24/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.3438 - val_loss: 0.6514\n",
            "Epoch 25/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.3410 - val_loss: 0.6455\n",
            "Epoch 26/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3388 - val_loss: 0.6395\n",
            "Epoch 27/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.3357 - val_loss: 0.6337\n",
            "Epoch 28/200\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 0.3331 - val_loss: 0.6322\n",
            "Epoch 29/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.3300 - val_loss: 0.6290\n",
            "Epoch 30/200\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 0.3273 - val_loss: 0.6285\n",
            "Epoch 31/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.3244 - val_loss: 0.6244\n",
            "Epoch 32/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.3209 - val_loss: 0.6206\n",
            "Epoch 33/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3178 - val_loss: 0.6177\n",
            "Epoch 34/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3148 - val_loss: 0.6148\n",
            "Epoch 35/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.3117 - val_loss: 0.6113\n",
            "Epoch 36/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.3083 - val_loss: 0.6097\n",
            "Epoch 37/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.3048 - val_loss: 0.6051\n",
            "Epoch 38/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.3014 - val_loss: 0.6000\n",
            "Epoch 39/200\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.2982 - val_loss: 0.5928\n",
            "Epoch 40/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.2941 - val_loss: 0.5888\n",
            "Epoch 41/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.2908 - val_loss: 0.5866\n",
            "Epoch 42/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.2869 - val_loss: 0.5782\n",
            "Epoch 43/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.2831 - val_loss: 0.5737\n",
            "Epoch 44/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.2792 - val_loss: 0.5645\n",
            "Epoch 45/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.2756 - val_loss: 0.5571\n",
            "Epoch 46/200\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.2718 - val_loss: 0.5501\n",
            "Epoch 47/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.2674 - val_loss: 0.5450\n",
            "Epoch 48/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2636 - val_loss: 0.5337\n",
            "Epoch 49/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.2595 - val_loss: 0.5258\n",
            "Epoch 50/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.2555 - val_loss: 0.5198\n",
            "Epoch 51/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.2518 - val_loss: 0.5117\n",
            "Epoch 52/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.2481 - val_loss: 0.5031\n",
            "Epoch 53/200\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 0.2439 - val_loss: 0.4949\n",
            "Epoch 54/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.2400 - val_loss: 0.4883\n",
            "Epoch 55/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.2360 - val_loss: 0.4835\n",
            "Epoch 56/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.2317 - val_loss: 0.4744\n",
            "Epoch 57/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.2278 - val_loss: 0.4626\n",
            "Epoch 58/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2241 - val_loss: 0.4540\n",
            "Epoch 59/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.2197 - val_loss: 0.4460\n",
            "Epoch 60/200\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.2169 - val_loss: 0.4383\n",
            "Epoch 61/200\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 0.2123 - val_loss: 0.4297\n",
            "Epoch 62/200\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 0.2094 - val_loss: 0.4231\n",
            "Epoch 63/200\n",
            "8/8 [==============================] - 0s 31ms/step - loss: 0.2053 - val_loss: 0.4172\n",
            "Epoch 64/200\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 0.2009 - val_loss: 0.4082\n",
            "Epoch 65/200\n",
            "8/8 [==============================] - 0s 32ms/step - loss: 0.1973 - val_loss: 0.3987\n",
            "Epoch 66/200\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 0.1936 - val_loss: 0.3937\n",
            "Epoch 67/200\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 0.1897 - val_loss: 0.3890\n",
            "Epoch 68/200\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 0.1860 - val_loss: 0.3780\n",
            "Epoch 69/200\n",
            "8/8 [==============================] - 0s 27ms/step - loss: 0.1823 - val_loss: 0.3707\n",
            "Epoch 70/200\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 0.1793 - val_loss: 0.3615\n",
            "Epoch 71/200\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 0.1756 - val_loss: 0.3514\n",
            "Epoch 72/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1723 - val_loss: 0.3410\n",
            "Epoch 73/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.1685 - val_loss: 0.3383\n",
            "Epoch 74/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.1659 - val_loss: 0.3369\n",
            "Epoch 75/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1624 - val_loss: 0.3226\n",
            "Epoch 76/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.1592 - val_loss: 0.3139\n",
            "Epoch 77/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.1560 - val_loss: 0.3037\n",
            "Epoch 78/200\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.1534 - val_loss: 0.3011\n",
            "Epoch 79/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1502 - val_loss: 0.2944\n",
            "Epoch 80/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.1465 - val_loss: 0.2864\n",
            "Epoch 81/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.1437 - val_loss: 0.2819\n",
            "Epoch 82/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.1414 - val_loss: 0.2753\n",
            "Epoch 83/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.1383 - val_loss: 0.2692\n",
            "Epoch 84/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.1353 - val_loss: 0.2619\n",
            "Epoch 85/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.1336 - val_loss: 0.2563\n",
            "Epoch 86/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.1312 - val_loss: 0.2477\n",
            "Epoch 87/200\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.1280 - val_loss: 0.2446\n",
            "Epoch 88/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.1254 - val_loss: 0.2428\n",
            "Epoch 89/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1233 - val_loss: 0.2369\n",
            "Epoch 90/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1208 - val_loss: 0.2286\n",
            "Epoch 91/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.1185 - val_loss: 0.2239\n",
            "Epoch 92/200\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.1168 - val_loss: 0.2214\n",
            "Epoch 93/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.1142 - val_loss: 0.2172\n",
            "Epoch 94/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1123 - val_loss: 0.2093\n",
            "Epoch 95/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1101 - val_loss: 0.2043\n",
            "Epoch 96/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.1086 - val_loss: 0.2007\n",
            "Epoch 97/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.1061 - val_loss: 0.1964\n",
            "Epoch 98/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.1045 - val_loss: 0.1926\n",
            "Epoch 99/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.1027 - val_loss: 0.1876\n",
            "Epoch 100/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.1008 - val_loss: 0.1839\n",
            "Epoch 101/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0992 - val_loss: 0.1811\n",
            "Epoch 102/200\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0972 - val_loss: 0.1761\n",
            "Epoch 103/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0956 - val_loss: 0.1751\n",
            "Epoch 104/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0941 - val_loss: 0.1704\n",
            "Epoch 105/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0924 - val_loss: 0.1639\n",
            "Epoch 106/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0920 - val_loss: 0.1614\n",
            "Epoch 107/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.0900 - val_loss: 0.1632\n",
            "Epoch 108/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0882 - val_loss: 0.1557\n",
            "Epoch 109/200\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0874 - val_loss: 0.1509\n",
            "Epoch 110/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0860 - val_loss: 0.1518\n",
            "Epoch 111/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0845 - val_loss: 0.1496\n",
            "Epoch 112/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0831 - val_loss: 0.1461\n",
            "Epoch 113/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0815 - val_loss: 0.1414\n",
            "Epoch 114/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0801 - val_loss: 0.1400\n",
            "Epoch 115/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0788 - val_loss: 0.1390\n",
            "Epoch 116/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0778 - val_loss: 0.1364\n",
            "Epoch 117/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.0767 - val_loss: 0.1317\n",
            "Epoch 118/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0754 - val_loss: 0.1294\n",
            "Epoch 119/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0747 - val_loss: 0.1285\n",
            "Epoch 120/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0735 - val_loss: 0.1266\n",
            "Epoch 121/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0725 - val_loss: 0.1227\n",
            "Epoch 122/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0716 - val_loss: 0.1208\n",
            "Epoch 123/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0707 - val_loss: 0.1203\n",
            "Epoch 124/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0696 - val_loss: 0.1198\n",
            "Epoch 125/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.0682 - val_loss: 0.1157\n",
            "Epoch 126/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0674 - val_loss: 0.1118\n",
            "Epoch 127/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0662 - val_loss: 0.1109\n",
            "Epoch 128/200\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0659 - val_loss: 0.1108\n",
            "Epoch 129/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0645 - val_loss: 0.1092\n",
            "Epoch 130/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0644 - val_loss: 0.1063\n",
            "Epoch 131/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.0627 - val_loss: 0.1051\n",
            "Epoch 132/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0624 - val_loss: 0.1037\n",
            "Epoch 133/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0616 - val_loss: 0.0980\n",
            "Epoch 134/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0604 - val_loss: 0.0983\n",
            "Epoch 135/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0596 - val_loss: 0.0983\n",
            "Epoch 136/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0589 - val_loss: 0.0984\n",
            "Epoch 137/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0580 - val_loss: 0.0962\n",
            "Epoch 138/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0573 - val_loss: 0.0946\n",
            "Epoch 139/200\n",
            "8/8 [==============================] - 0s 25ms/step - loss: 0.0568 - val_loss: 0.0920\n",
            "Epoch 140/200\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 0.0559 - val_loss: 0.0924\n",
            "Epoch 141/200\n",
            "8/8 [==============================] - 0s 25ms/step - loss: 0.0553 - val_loss: 0.0909\n",
            "Epoch 142/200\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 0.0547 - val_loss: 0.0891\n",
            "Epoch 143/200\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 0.0544 - val_loss: 0.0865\n",
            "Epoch 144/200\n",
            "8/8 [==============================] - 0s 31ms/step - loss: 0.0534 - val_loss: 0.0861\n",
            "Epoch 145/200\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 0.0530 - val_loss: 0.0856\n",
            "Epoch 146/200\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 0.0522 - val_loss: 0.0850\n",
            "Epoch 147/200\n",
            "8/8 [==============================] - 0s 30ms/step - loss: 0.0517 - val_loss: 0.0832\n",
            "Epoch 148/200\n",
            "8/8 [==============================] - 0s 29ms/step - loss: 0.0512 - val_loss: 0.0822\n",
            "Epoch 149/200\n",
            "8/8 [==============================] - 0s 28ms/step - loss: 0.0504 - val_loss: 0.0813\n",
            "Epoch 150/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.0500 - val_loss: 0.0802\n",
            "Epoch 151/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.0495 - val_loss: 0.0788\n",
            "Epoch 152/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0489 - val_loss: 0.0774\n",
            "Epoch 153/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0482 - val_loss: 0.0769\n",
            "Epoch 154/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0481 - val_loss: 0.0768\n",
            "Epoch 155/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0476 - val_loss: 0.0757\n",
            "Epoch 156/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0471 - val_loss: 0.0747\n",
            "Epoch 157/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.0465 - val_loss: 0.0741\n",
            "Epoch 158/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0463 - val_loss: 0.0741\n",
            "Epoch 159/200\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.0456 - val_loss: 0.0712\n",
            "Epoch 160/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0452 - val_loss: 0.0708\n",
            "Epoch 161/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0447 - val_loss: 0.0703\n",
            "Epoch 162/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0443 - val_loss: 0.0706\n",
            "Epoch 163/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0442 - val_loss: 0.0711\n",
            "Epoch 164/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0435 - val_loss: 0.0692\n",
            "Epoch 165/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0438 - val_loss: 0.0664\n",
            "Epoch 166/200\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0429 - val_loss: 0.0684\n",
            "Epoch 167/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0427 - val_loss: 0.0682\n",
            "Epoch 168/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0422 - val_loss: 0.0659\n",
            "Epoch 169/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0420 - val_loss: 0.0655\n",
            "Epoch 170/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0417 - val_loss: 0.0630\n",
            "Epoch 171/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0410 - val_loss: 0.0641\n",
            "Epoch 172/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0409 - val_loss: 0.0646\n",
            "Epoch 173/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0405 - val_loss: 0.0648\n",
            "Epoch 174/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0402 - val_loss: 0.0629\n",
            "Epoch 175/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0400 - val_loss: 0.0609\n",
            "Epoch 176/200\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 0.0398 - val_loss: 0.0625\n",
            "Epoch 177/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.0394 - val_loss: 0.0609\n",
            "Epoch 178/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0393 - val_loss: 0.0607\n",
            "Epoch 179/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0387 - val_loss: 0.0606\n",
            "Epoch 180/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0385 - val_loss: 0.0596\n",
            "Epoch 181/200\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 0.0382 - val_loss: 0.0593\n",
            "Epoch 182/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0377 - val_loss: 0.0591\n",
            "Epoch 183/200\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 0.0377 - val_loss: 0.0585\n",
            "Epoch 184/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0376 - val_loss: 0.0574\n",
            "Epoch 185/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0372 - val_loss: 0.0575\n",
            "Epoch 186/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0371 - val_loss: 0.0570\n",
            "Epoch 187/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0368 - val_loss: 0.0567\n",
            "Epoch 188/200\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0365 - val_loss: 0.0573\n",
            "Epoch 189/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0363 - val_loss: 0.0555\n",
            "Epoch 190/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0361 - val_loss: 0.0556\n",
            "Epoch 191/200\n",
            "8/8 [==============================] - 0s 19ms/step - loss: 0.0361 - val_loss: 0.0572\n",
            "Epoch 192/200\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.0356 - val_loss: 0.0551\n",
            "Epoch 193/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0354 - val_loss: 0.0549\n",
            "Epoch 194/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0351 - val_loss: 0.0544\n",
            "Epoch 195/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0352 - val_loss: 0.0546\n",
            "Epoch 196/200\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0350 - val_loss: 0.0537\n",
            "Epoch 197/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0345 - val_loss: 0.0530\n",
            "Epoch 198/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0343 - val_loss: 0.0543\n",
            "Epoch 199/200\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0341 - val_loss: 0.0528\n",
            "Epoch 200/200\n",
            "8/8 [==============================] - 0s 17ms/step - loss: 0.0340 - val_loss: 0.0521\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(r.history['loss'],label='loss')\n",
        "plt.plot(r.history['val_loss'],label='val_loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "cnK6z37eCTrS",
        "outputId": "b7809390-f9e5-4348-b8e6-52b2cf9837b3"
      },
      "execution_count": 7,
      "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": [
        "#one step forecast using true targets\n",
        "outputs=model.predict(X)\n",
        "print(outputs.shape)\n",
        "predictions=outputs[:,0]\n",
        "\n",
        "plt.plot(Y, label='targets')\n",
        "plt.plot(predictions, label='predictions')\n",
        "plt.title(\"SimpleRNN\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "U2IZBYx5E0YE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "3d10ba8c-6fde-480c-fbfd-5153df0a73c5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 3ms/step\n",
            "(490, 1)\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": [
        "#multistep forecast\n",
        "#if we want to forecast next value\n",
        "forecast=[]\n",
        "input_=X[-N//2]\n",
        "while len(forecast)<len(Y[-N//2:]):\n",
        "  f=model.predict(input_.reshape(1,T,1))[0,0]\n",
        "  forecast.append(f)\n",
        "  #make new input with latest forecast\n",
        "  input_=np.roll(input_,-1)\n",
        "  input_[-1]=f\n",
        "\n",
        "plt.plot(Y[-N//2:],label='target')\n",
        "plt.plot(forecast,label='forecast')\n",
        "plt.title(\"RNN Forecast\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "THZePZ_ciXWF",
        "outputId": "a8fa3fc5-7ce7-4726-b1ea-1b8731c06fca"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
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            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
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            "1/1 [==============================] - 0s 31ms/step\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"
          }
        }
      ]
    }
  ]
}