Linear_Regression_part_2.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN6QfsNExGCfOyAPPNJGLb8",
      "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/tusharbansal22/Tensorflow-Notebooks/blob/main/Linear_Regression_part_2.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": "md1feexSImhA"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "g2cYUe6NJl5G"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/moore.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vUEyqY8J0Nc",
        "outputId": "e126afdd-05e8-4529-9724-44767543c67e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-23 15:52:43--  https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/moore.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2302 (2.2K) [text/plain]\n",
            "Saving to: ‘moore.csv’\n",
            "\n",
            "\rmoore.csv             0%[                    ]       0  --.-KB/s               \rmoore.csv           100%[===================>]   2.25K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-01-23 15:52:43 (44.0 MB/s) - ‘moore.csv’ saved [2302/2302]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('moore.csv',header=None).to_numpy()"
      ],
      "metadata": {
        "id": "WwQbqLi2KO4J"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WC7rYzrIKejC",
        "outputId": "83acb1ca-eecf-4e56-aa36-ff3187c04e99"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=data[:,0].reshape(-1,1)\n",
        "Y=data[:,1]"
      ],
      "metadata": {
        "id": "G8zbiUGbvygk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X,Y);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "YXE80QdZwPba",
        "outputId": "ef385ae3-767a-4d68-e3af-3f2cd33b61df"
      },
      "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": [
        "Y=np.log(Y)\n",
        "plt.scatter(X,Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "BWnIVv4JxFBF",
        "outputId": "207b7b71-6b15-49e5-ad7e-a3a0fb2dd0e1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f83970cfdc0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "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": [
        "X=X-X.mean()"
      ],
      "metadata": {
        "id": "urmv0E6cxUqC"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=tf.keras.models.Sequential(\n",
        "    [tf.keras.layers.Input(shape=(1,)),\n",
        "    tf.keras.layers.Dense(1)]\n",
        ")"
      ],
      "metadata": {
        "id": "OrUZnGh1xjt8"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.SGD(0.001,0.9),\n",
        "    loss='mse',\n",
        ")"
      ],
      "metadata": {
        "id": "HgLlE4btyBSF"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def schedule(epoch, lr):\n",
        "  if epoch>=50:\n",
        "     return 0.0001\n",
        "  return 0.001\n",
        "\n",
        "scheduler = tf.keras.callbacks.LearningRateScheduler(schedule)"
      ],
      "metadata": {
        "id": "tTOm0P6tyoIx"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r= model.fit(X,Y,epochs=200, callbacks=[scheduler])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5KShvJKnzIXZ",
        "outputId": "c8beea0d-d93c-4c0f-8c97-0d1d5ddc4af5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "6/6 [==============================] - 1s 3ms/step - loss: 488.2142 - lr: 0.0010\n",
            "Epoch 2/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 396.0021 - lr: 0.0010\n",
            "Epoch 3/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 290.7985 - lr: 0.0010\n",
            "Epoch 4/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 235.4502 - lr: 0.0010\n",
            "Epoch 5/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 183.5429 - lr: 0.0010\n",
            "Epoch 6/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 135.2477 - lr: 0.0010\n",
            "Epoch 7/200\n",
            "6/6 [==============================] - 0s 52ms/step - loss: 102.5460 - lr: 0.0010\n",
            "Epoch 8/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 74.2132 - lr: 0.0010\n",
            "Epoch 9/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 54.4494 - lr: 0.0010\n",
            "Epoch 10/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 38.6562 - lr: 0.0010\n",
            "Epoch 11/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 31.6543 - lr: 0.0010\n",
            "Epoch 12/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 21.6004 - lr: 0.0010\n",
            "Epoch 13/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 18.0558 - lr: 0.0010\n",
            "Epoch 14/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 14.8387 - lr: 0.0010\n",
            "Epoch 15/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 11.2290 - lr: 0.0010\n",
            "Epoch 16/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 9.6700 - lr: 0.0010\n",
            "Epoch 17/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 8.5368 - lr: 0.0010\n",
            "Epoch 18/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 7.2957 - lr: 0.0010\n",
            "Epoch 19/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 6.2069 - lr: 0.0010\n",
            "Epoch 20/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 3.9368 - lr: 0.0010\n",
            "Epoch 21/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 2.8674 - lr: 0.0010\n",
            "Epoch 22/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 3.9981 - lr: 0.0010\n",
            "Epoch 23/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 3.1203 - lr: 0.0010\n",
            "Epoch 24/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.9485 - lr: 0.0010\n",
            "Epoch 25/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 2.3522 - lr: 0.0010\n",
            "Epoch 26/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 2.1584 - lr: 0.0010\n",
            "Epoch 27/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.6409 - lr: 0.0010\n",
            "Epoch 28/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.4542 - lr: 0.0010\n",
            "Epoch 29/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.1756 - lr: 0.0010\n",
            "Epoch 30/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0540 - lr: 0.0010\n",
            "Epoch 31/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0590 - lr: 0.0010\n",
            "Epoch 32/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.1345 - lr: 0.0010\n",
            "Epoch 33/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9567 - lr: 0.0010\n",
            "Epoch 34/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9722 - lr: 0.0010\n",
            "Epoch 35/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.0927 - lr: 0.0010\n",
            "Epoch 36/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.0476 - lr: 0.0010\n",
            "Epoch 37/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9872 - lr: 0.0010\n",
            "Epoch 38/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9679 - lr: 0.0010\n",
            "Epoch 39/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.1117 - lr: 0.0010\n",
            "Epoch 40/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0812 - lr: 0.0010\n",
            "Epoch 41/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9093 - lr: 0.0010\n",
            "Epoch 42/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9059 - lr: 0.0010\n",
            "Epoch 43/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9321 - lr: 0.0010\n",
            "Epoch 44/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9748 - lr: 0.0010\n",
            "Epoch 45/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8779 - lr: 0.0010\n",
            "Epoch 46/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9164 - lr: 0.0010\n",
            "Epoch 47/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8878 - lr: 0.0010\n",
            "Epoch 48/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8846 - lr: 0.0010\n",
            "Epoch 49/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8865 - lr: 0.0010\n",
            "Epoch 50/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9359 - lr: 0.0010\n",
            "Epoch 51/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0240 - lr: 1.0000e-04\n",
            "Epoch 52/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9934 - lr: 1.0000e-04\n",
            "Epoch 53/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8811 - lr: 1.0000e-04\n",
            "Epoch 54/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.9156 - lr: 1.0000e-04\n",
            "Epoch 55/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8982 - lr: 1.0000e-04\n",
            "Epoch 56/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8759 - lr: 1.0000e-04\n",
            "Epoch 57/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8905 - lr: 1.0000e-04\n",
            "Epoch 58/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8721 - lr: 1.0000e-04\n",
            "Epoch 59/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8961 - lr: 1.0000e-04\n",
            "Epoch 60/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8728 - lr: 1.0000e-04\n",
            "Epoch 61/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8706 - lr: 1.0000e-04\n",
            "Epoch 62/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8731 - lr: 1.0000e-04\n",
            "Epoch 63/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8800 - lr: 1.0000e-04\n",
            "Epoch 64/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8748 - lr: 1.0000e-04\n",
            "Epoch 65/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8700 - lr: 1.0000e-04\n",
            "Epoch 66/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8700 - lr: 1.0000e-04\n",
            "Epoch 67/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8796 - lr: 1.0000e-04\n",
            "Epoch 68/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8728 - lr: 1.0000e-04\n",
            "Epoch 69/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8749 - lr: 1.0000e-04\n",
            "Epoch 70/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8753 - lr: 1.0000e-04\n",
            "Epoch 71/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8679 - lr: 1.0000e-04\n",
            "Epoch 72/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8727 - lr: 1.0000e-04\n",
            "Epoch 73/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8739 - lr: 1.0000e-04\n",
            "Epoch 74/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8901 - lr: 1.0000e-04\n",
            "Epoch 75/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8712 - lr: 1.0000e-04\n",
            "Epoch 76/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8717 - lr: 1.0000e-04\n",
            "Epoch 77/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8718 - lr: 1.0000e-04\n",
            "Epoch 78/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8712 - lr: 1.0000e-04\n",
            "Epoch 79/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8733 - lr: 1.0000e-04\n",
            "Epoch 80/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8711 - lr: 1.0000e-04\n",
            "Epoch 81/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8756 - lr: 1.0000e-04\n",
            "Epoch 82/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8795 - lr: 1.0000e-04\n",
            "Epoch 83/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8896 - lr: 1.0000e-04\n",
            "Epoch 84/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8799 - lr: 1.0000e-04\n",
            "Epoch 85/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9043 - lr: 1.0000e-04\n",
            "Epoch 86/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8782 - lr: 1.0000e-04\n",
            "Epoch 87/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8709 - lr: 1.0000e-04\n",
            "Epoch 88/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8726 - lr: 1.0000e-04\n",
            "Epoch 89/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8762 - lr: 1.0000e-04\n",
            "Epoch 90/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8720 - lr: 1.0000e-04\n",
            "Epoch 91/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8720 - lr: 1.0000e-04\n",
            "Epoch 92/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8711 - lr: 1.0000e-04\n",
            "Epoch 93/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8751 - lr: 1.0000e-04\n",
            "Epoch 94/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8764 - lr: 1.0000e-04\n",
            "Epoch 95/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8713 - lr: 1.0000e-04\n",
            "Epoch 96/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8750 - lr: 1.0000e-04\n",
            "Epoch 97/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8736 - lr: 1.0000e-04\n",
            "Epoch 98/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8721 - lr: 1.0000e-04\n",
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            "Epoch 100/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8851 - lr: 1.0000e-04\n",
            "Epoch 101/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8802 - lr: 1.0000e-04\n",
            "Epoch 102/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8751 - lr: 1.0000e-04\n",
            "Epoch 103/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8737 - lr: 1.0000e-04\n",
            "Epoch 104/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9275 - lr: 1.0000e-04\n",
            "Epoch 105/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9624 - lr: 1.0000e-04\n",
            "Epoch 106/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8857 - lr: 1.0000e-04\n",
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            "6/6 [==============================] - 0s 2ms/step - loss: 0.9208 - lr: 1.0000e-04\n",
            "Epoch 108/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9312 - lr: 1.0000e-04\n",
            "Epoch 109/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8589 - lr: 1.0000e-04\n",
            "Epoch 110/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9490 - lr: 1.0000e-04\n",
            "Epoch 111/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9292 - lr: 1.0000e-04\n",
            "Epoch 112/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8624 - lr: 1.0000e-04\n",
            "Epoch 113/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9333 - lr: 1.0000e-04\n",
            "Epoch 114/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8890 - lr: 1.0000e-04\n",
            "Epoch 115/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8712 - lr: 1.0000e-04\n",
            "Epoch 116/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8903 - lr: 1.0000e-04\n",
            "Epoch 117/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8890 - lr: 1.0000e-04\n",
            "Epoch 118/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8705 - lr: 1.0000e-04\n",
            "Epoch 119/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9246 - lr: 1.0000e-04\n",
            "Epoch 120/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8939 - lr: 1.0000e-04\n",
            "Epoch 121/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8799 - lr: 1.0000e-04\n",
            "Epoch 122/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8799 - lr: 1.0000e-04\n",
            "Epoch 123/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8734 - lr: 1.0000e-04\n",
            "Epoch 124/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8738 - lr: 1.0000e-04\n",
            "Epoch 125/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8707 - lr: 1.0000e-04\n",
            "Epoch 126/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8724 - lr: 1.0000e-04\n",
            "Epoch 127/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8803 - lr: 1.0000e-04\n",
            "Epoch 128/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8785 - lr: 1.0000e-04\n",
            "Epoch 129/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8721 - lr: 1.0000e-04\n",
            "Epoch 130/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8755 - lr: 1.0000e-04\n",
            "Epoch 131/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9032 - lr: 1.0000e-04\n",
            "Epoch 132/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8766 - lr: 1.0000e-04\n",
            "Epoch 133/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8776 - lr: 1.0000e-04\n",
            "Epoch 134/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8871 - lr: 1.0000e-04\n",
            "Epoch 135/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8729 - lr: 1.0000e-04\n",
            "Epoch 136/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8716 - lr: 1.0000e-04\n",
            "Epoch 137/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8792 - lr: 1.0000e-04\n",
            "Epoch 138/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8717 - lr: 1.0000e-04\n",
            "Epoch 139/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8756 - lr: 1.0000e-04\n",
            "Epoch 140/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8913 - lr: 1.0000e-04\n",
            "Epoch 141/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8888 - lr: 1.0000e-04\n",
            "Epoch 142/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8771 - lr: 1.0000e-04\n",
            "Epoch 143/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8712 - lr: 1.0000e-04\n",
            "Epoch 144/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8785 - lr: 1.0000e-04\n",
            "Epoch 145/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8737 - lr: 1.0000e-04\n",
            "Epoch 146/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8768 - lr: 1.0000e-04\n",
            "Epoch 147/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8782 - lr: 1.0000e-04\n",
            "Epoch 148/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8734 - lr: 1.0000e-04\n",
            "Epoch 149/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8700 - lr: 1.0000e-04\n",
            "Epoch 150/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9266 - lr: 1.0000e-04\n",
            "Epoch 151/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9283 - lr: 1.0000e-04\n",
            "Epoch 152/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8819 - lr: 1.0000e-04\n",
            "Epoch 153/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9468 - lr: 1.0000e-04\n",
            "Epoch 154/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8797 - lr: 1.0000e-04\n",
            "Epoch 155/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8901 - lr: 1.0000e-04\n",
            "Epoch 156/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9140 - lr: 1.0000e-04\n",
            "Epoch 157/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8828 - lr: 1.0000e-04\n",
            "Epoch 158/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8747 - lr: 1.0000e-04\n",
            "Epoch 159/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8900 - lr: 1.0000e-04\n",
            "Epoch 160/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8898 - lr: 1.0000e-04\n",
            "Epoch 161/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8693 - lr: 1.0000e-04\n",
            "Epoch 162/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8754 - lr: 1.0000e-04\n",
            "Epoch 163/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8790 - lr: 1.0000e-04\n",
            "Epoch 164/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8715 - lr: 1.0000e-04\n",
            "Epoch 165/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8726 - lr: 1.0000e-04\n",
            "Epoch 166/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8736 - lr: 1.0000e-04\n",
            "Epoch 167/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8727 - lr: 1.0000e-04\n",
            "Epoch 168/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8729 - lr: 1.0000e-04\n",
            "Epoch 169/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8778 - lr: 1.0000e-04\n",
            "Epoch 170/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8854 - lr: 1.0000e-04\n",
            "Epoch 171/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8740 - lr: 1.0000e-04\n",
            "Epoch 172/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8714 - lr: 1.0000e-04\n",
            "Epoch 173/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8731 - lr: 1.0000e-04\n",
            "Epoch 174/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8698 - lr: 1.0000e-04\n",
            "Epoch 175/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8718 - lr: 1.0000e-04\n",
            "Epoch 176/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8719 - lr: 1.0000e-04\n",
            "Epoch 177/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8712 - lr: 1.0000e-04\n",
            "Epoch 178/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8739 - lr: 1.0000e-04\n",
            "Epoch 179/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8985 - lr: 1.0000e-04\n",
            "Epoch 180/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9477 - lr: 1.0000e-04\n",
            "Epoch 181/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9094 - lr: 1.0000e-04\n",
            "Epoch 182/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8786 - lr: 1.0000e-04\n",
            "Epoch 183/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8720 - lr: 1.0000e-04\n",
            "Epoch 184/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8708 - lr: 1.0000e-04\n",
            "Epoch 185/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8793 - lr: 1.0000e-04\n",
            "Epoch 186/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8707 - lr: 1.0000e-04\n",
            "Epoch 187/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8729 - lr: 1.0000e-04\n",
            "Epoch 188/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8848 - lr: 1.0000e-04\n",
            "Epoch 189/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8977 - lr: 1.0000e-04\n",
            "Epoch 190/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8818 - lr: 1.0000e-04\n",
            "Epoch 191/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8703 - lr: 1.0000e-04\n",
            "Epoch 192/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8747 - lr: 1.0000e-04\n",
            "Epoch 193/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8786 - lr: 1.0000e-04\n",
            "Epoch 194/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8871 - lr: 1.0000e-04\n",
            "Epoch 195/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8808 - lr: 1.0000e-04\n",
            "Epoch 196/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8707 - lr: 1.0000e-04\n",
            "Epoch 197/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8705 - lr: 1.0000e-04\n",
            "Epoch 198/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8706 - lr: 1.0000e-04\n",
            "Epoch 199/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8726 - lr: 1.0000e-04\n",
            "Epoch 200/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8715 - lr: 1.0000e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(r.history['loss'], label='loss')\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "9PAU2fg2zWF_",
        "outputId": "104a1986-ace0-4569-9b1b-73f9ad644afe"
      },
      "execution_count": 15,
      "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": [
        "model.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6wXTzSwzuGk",
        "outputId": "0633901e-ab2d-4f1f-d139-de6b51e9c396"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.core.dense.Dense at 0x7f8397093a90>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers[0].get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CiLOtw9Az-Er",
        "outputId": "4ab2ae43-9818-4d77-a8b3-45c9affa9d8a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.3396166]], dtype=float32), array([17.756302], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a= model.layers[0].get_weights()[0][0,0]"
      ],
      "metadata": {
        "id": "TAiQKJoo0ECH"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Time to double:\", np.log(2)/a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZQZtHFO0STK",
        "outputId": "371750c1-56cf-40d4-faed-d162cdec6682"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time to double: 2.040969691403995\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " X=X.flatten()\n",
        " denominator= X.dot(X)- X.mean()*X.sum()\n",
        " a=(X.dot(Y)-Y.mean()*X.sum())/denominator\n",
        " b=(Y.mean( )*X.dot(X)-X.mean()*X.dot(Y))/denominator\n",
        " print(a,b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdOeE_2a-CZN",
        "outputId": "ae487c0a-c622-46ad-8ea5-457c5dbbe1a6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.341682487387313 17.764939393631764\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Time to double:\", np.log(2)/a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CakW4d3N--Ro",
        "outputId": "d4e6f21f-1c68-4f1f-ed97-c5b5839911d1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time to double: 2.0286295205239204\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 2 : Making Predictions"
      ],
      "metadata": {
        "id": "V0QS6D7UU9HK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Yhat=model.predict(X).flatten()\n",
        "plt.scatter(X,Y)\n",
        "plt.plot(X,Yhat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "faR5e0WfVBP_",
        "outputId": "91750e31-b60d-4043-d68f-2403b4275ba8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 0s 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8392639f70>]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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": [
        "w,b = model.layers[0].get_weights()\n",
        "X=X.reshape(-1,1)\n",
        "Yhat2=(X.dot(w)+b).flatten()\n",
        "np.allclose(Yhat,Yhat2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0k0bqToVL_j",
        "outputId": "898a359f-8633-4e2d-f23a-8f14b86c9a1c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    }
  ]
}