TF2_0_Linear_Regression.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNRMOxwMWvwa1PFa/N1ZqAH",
      "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/Gbell26/Tensor_Flow2.0_Course/blob/main/TF2_0_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hMCbM44RvZ9b"
      },
      "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\n"
      ],
      "metadata": {
        "id": "ptBSt1PtvlRo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Get data\n",
        "!wget https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/moore.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5rVg59Mvtag",
        "outputId": "e40bcead-b3ef-4724-a4ef-402fcf802702"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-12-21 16:04:48--  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, 185.199.110.133, 185.199.111.133, ...\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.1’\n",
            "\n",
            "\rmoore.csv.1           0%[                    ]       0  --.-KB/s               \rmoore.csv.1         100%[===================>]   2.25K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-12-21 16:04:48 (20.5 MB/s) - ‘moore.csv.1’ saved [2302/2302]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#load in data"
      ],
      "metadata": {
        "id": "MJKGgMY0v9O8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('moore.csv', header=None).to_numpy()"
      ],
      "metadata": {
        "id": "VK9ZDaqSwArs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnV1Odm8wR17",
        "outputId": "d1f8d070-8923-411d-df41-ac98721d4aac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data[:,0].reshape(-1,1) # making an N x D array\n",
        "Y = data[:,1]"
      ],
      "metadata": {
        "id": "qUSR244hwV8T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X,Y);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "8iBOlGBjwsa_",
        "outputId": "836e155f-4bc2-4be1-d380-7721e8d39b03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = np.log(Y)\n",
        "plt.scatter(X,Y);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "3rkWUlGtwwOd",
        "outputId": "843e9839-2752-45b1-c122-e264b677110b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Give zero mean by subtracting the mean OR divide by sd\n",
        "X = X - X.mean()\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nBs1tkVoVT72",
        "outputId": "891cc050-d68a-4e31-947d-10a844129805"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-31.19753086],\n",
              "       [-30.19753086],\n",
              "       [-29.19753086],\n",
              "       [-29.19753086],\n",
              "       [-28.19753086],\n",
              "       [-28.19753086],\n",
              "       [-28.19753086],\n",
              "       [-27.19753086],\n",
              "       [-26.19753086],\n",
              "       [-26.19753086],\n",
              "       [-26.19753086],\n",
              "       [-24.19753086],\n",
              "       [-24.19753086],\n",
              "       [-23.19753086],\n",
              "       [-23.19753086],\n",
              "       [-23.19753086],\n",
              "       [-21.19753086],\n",
              "       [-20.19753086],\n",
              "       [-20.19753086],\n",
              "       [-19.19753086],\n",
              "       [-18.19753086],\n",
              "       [-18.19753086],\n",
              "       [-17.19753086],\n",
              "       [-17.19753086],\n",
              "       [-17.19753086],\n",
              "       [-16.19753086],\n",
              "       [-16.19753086],\n",
              "       [-16.19753086],\n",
              "       [-15.19753086],\n",
              "       [-15.19753086],\n",
              "       [-15.19753086],\n",
              "       [-15.19753086],\n",
              "       [-14.19753086],\n",
              "       [-14.19753086],\n",
              "       [-13.19753086],\n",
              "       [-13.19753086],\n",
              "       [-13.19753086],\n",
              "       [-13.19753086],\n",
              "       [-12.19753086],\n",
              "       [-11.19753086],\n",
              "       [-11.19753086],\n",
              "       [-10.19753086],\n",
              "       [-10.19753086],\n",
              "       [ -9.19753086],\n",
              "       [ -9.19753086],\n",
              "       [ -8.19753086],\n",
              "       [ -8.19753086],\n",
              "       [ -7.19753086],\n",
              "       [ -3.19753086],\n",
              "       [ -7.19753086],\n",
              "       [ -6.19753086],\n",
              "       [ -5.19753086],\n",
              "       [ -5.19753086],\n",
              "       [ -5.19753086],\n",
              "       [ -4.19753086],\n",
              "       [ -3.19753086],\n",
              "       [ -3.19753086],\n",
              "       [ -2.19753086],\n",
              "       [ -2.19753086],\n",
              "       [ -3.19753086],\n",
              "       [ -3.19753086],\n",
              "       [ -3.19753086],\n",
              "       [ -2.19753086],\n",
              "       [ -1.19753086],\n",
              "       [ -1.19753086],\n",
              "       [ -0.19753086],\n",
              "       [  1.80246914],\n",
              "       [  1.80246914],\n",
              "       [  2.80246914],\n",
              "       [  3.80246914],\n",
              "       [  2.80246914],\n",
              "       [  3.80246914],\n",
              "       [  4.80246914],\n",
              "       [  5.80246914],\n",
              "       [  0.80246914],\n",
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              "       [ 15.80246914],\n",
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              "       [ 15.80246914],\n",
              "       [ 15.80246914]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "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": "f2w96k8tV9qw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.SGD(0.001,0.9),\n",
        "    loss='mse',\n",
        ")"
      ],
      "metadata": {
        "id": "KTLxxjduWOp3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def schedule(epoch, lr):\n",
        "  if epoch >= 50:\n",
        "    return 0.0001\n",
        "  return 0.001\n",
        "scheduler = tf.keras.callbacks.LearningRateScheduler(schedule)"
      ],
      "metadata": {
        "id": "v5wisFThWp8b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r = model.fit(X,Y,epochs=200, callbacks=[scheduler])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4oQ5RpOSW_82",
        "outputId": "24e99c02-6a8c-4bef-a2e8-922d5d3973bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "6/6 [==============================] - 1s 4ms/step - loss: 334.9552 - lr: 0.0010\n",
            "Epoch 2/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 332.6212 - lr: 0.0010\n",
            "Epoch 3/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 314.8087 - lr: 0.0010\n",
            "Epoch 4/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 293.9137 - lr: 0.0010\n",
            "Epoch 5/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 206.5495 - lr: 0.0010\n",
            "Epoch 6/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 137.4301 - lr: 0.0010\n",
            "Epoch 7/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 120.4104 - lr: 0.0010\n",
            "Epoch 8/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 111.9497 - lr: 0.0010\n",
            "Epoch 9/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 76.5131 - lr: 0.0010\n",
            "Epoch 10/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 53.8449 - lr: 0.0010\n",
            "Epoch 11/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 43.1369 - lr: 0.0010\n",
            "Epoch 12/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 31.3316 - lr: 0.0010\n",
            "Epoch 13/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 27.9652 - lr: 0.0010\n",
            "Epoch 14/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 15.1611 - lr: 0.0010\n",
            "Epoch 15/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 12.0397 - lr: 0.0010\n",
            "Epoch 16/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 13.8893 - lr: 0.0010\n",
            "Epoch 17/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 7.7628 - lr: 0.0010\n",
            "Epoch 18/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 4.8109 - lr: 0.0010\n",
            "Epoch 19/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 4.9500 - lr: 0.0010\n",
            "Epoch 20/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 3.4426 - lr: 0.0010\n",
            "Epoch 21/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 2.7113 - lr: 0.0010\n",
            "Epoch 22/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 2.0609 - lr: 0.0010\n",
            "Epoch 23/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 2.1431 - lr: 0.0010\n",
            "Epoch 24/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.6148 - lr: 0.0010\n",
            "Epoch 25/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.3554 - lr: 0.0010\n",
            "Epoch 26/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 1.2681 - lr: 0.0010\n",
            "Epoch 27/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 2.1993 - lr: 0.0010\n",
            "Epoch 28/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.7806 - lr: 0.0010\n",
            "Epoch 29/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 1.4300 - lr: 0.0010\n",
            "Epoch 30/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 1.4652 - lr: 0.0010\n",
            "Epoch 31/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.9360 - lr: 0.0010\n",
            "Epoch 32/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.9091 - lr: 0.0010\n",
            "Epoch 33/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.9266 - lr: 0.0010\n",
            "Epoch 34/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8968 - lr: 0.0010\n",
            "Epoch 35/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.9275 - lr: 0.0010\n",
            "Epoch 36/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.9241 - lr: 0.0010\n",
            "Epoch 37/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.9264 - lr: 0.0010\n",
            "Epoch 38/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8864 - lr: 0.0010\n",
            "Epoch 39/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.9078 - lr: 0.0010\n",
            "Epoch 40/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.9001 - lr: 0.0010\n",
            "Epoch 41/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8928 - lr: 0.0010\n",
            "Epoch 42/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 1.1379 - lr: 0.0010\n",
            "Epoch 43/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.0976 - lr: 0.0010\n",
            "Epoch 44/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 1.1952 - lr: 0.0010\n",
            "Epoch 45/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.9861 - lr: 0.0010\n",
            "Epoch 46/200\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.9733 - lr: 0.0010\n",
            "Epoch 47/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.9497 - lr: 0.0010\n",
            "Epoch 48/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8880 - lr: 0.0010\n",
            "Epoch 49/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.9147 - lr: 0.0010\n",
            "Epoch 50/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8975 - lr: 0.0010\n",
            "Epoch 51/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 3.0772 - lr: 1.0000e-04\n",
            "Epoch 52/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 4.3598 - lr: 1.0000e-04\n",
            "Epoch 53/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.3373 - lr: 1.0000e-04\n",
            "Epoch 54/200\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 1.2654 - lr: 1.0000e-04\n",
            "Epoch 55/200\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 1.2125 - lr: 1.0000e-04\n",
            "Epoch 56/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8576 - lr: 1.0000e-04\n",
            "Epoch 57/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.1356 - lr: 1.0000e-04\n",
            "Epoch 58/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.9689 - lr: 1.0000e-04\n",
            "Epoch 59/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8771 - lr: 1.0000e-04\n",
            "Epoch 60/200\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.9004 - lr: 1.0000e-04\n",
            "Epoch 61/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8752 - lr: 1.0000e-04\n",
            "Epoch 62/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8839 - lr: 1.0000e-04\n",
            "Epoch 63/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8712 - lr: 1.0000e-04\n",
            "Epoch 64/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8853 - lr: 1.0000e-04\n",
            "Epoch 65/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8798 - lr: 1.0000e-04\n",
            "Epoch 66/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8684 - lr: 1.0000e-04\n",
            "Epoch 67/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8735 - lr: 1.0000e-04\n",
            "Epoch 68/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8846 - lr: 1.0000e-04\n",
            "Epoch 69/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8735 - lr: 1.0000e-04\n",
            "Epoch 70/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8743 - lr: 1.0000e-04\n",
            "Epoch 71/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8724 - lr: 1.0000e-04\n",
            "Epoch 72/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8906 - lr: 1.0000e-04\n",
            "Epoch 73/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8826 - lr: 1.0000e-04\n",
            "Epoch 74/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8835 - lr: 1.0000e-04\n",
            "Epoch 75/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8717 - lr: 1.0000e-04\n",
            "Epoch 76/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8919 - lr: 1.0000e-04\n",
            "Epoch 77/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8743 - lr: 1.0000e-04\n",
            "Epoch 78/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8828 - lr: 1.0000e-04\n",
            "Epoch 79/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8943 - lr: 1.0000e-04\n",
            "Epoch 80/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8796 - lr: 1.0000e-04\n",
            "Epoch 81/200\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.8745 - lr: 1.0000e-04\n",
            "Epoch 82/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8753 - lr: 1.0000e-04\n",
            "Epoch 83/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8746 - lr: 1.0000e-04\n",
            "Epoch 84/200\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.8755 - lr: 1.0000e-04\n",
            "Epoch 85/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8718 - lr: 1.0000e-04\n",
            "Epoch 86/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8717 - lr: 1.0000e-04\n",
            "Epoch 87/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8717 - lr: 1.0000e-04\n",
            "Epoch 88/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8725 - lr: 1.0000e-04\n",
            "Epoch 89/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8763 - lr: 1.0000e-04\n",
            "Epoch 90/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8777 - lr: 1.0000e-04\n",
            "Epoch 91/200\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.8679 - lr: 1.0000e-04\n",
            "Epoch 92/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8770 - lr: 1.0000e-04\n",
            "Epoch 93/200\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.8727 - lr: 1.0000e-04\n",
            "Epoch 94/200\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.8726 - lr: 1.0000e-04\n",
            "Epoch 95/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8789 - lr: 1.0000e-04\n",
            "Epoch 96/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8733 - lr: 1.0000e-04\n",
            "Epoch 97/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8733 - lr: 1.0000e-04\n",
            "Epoch 98/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8715 - lr: 1.0000e-04\n",
            "Epoch 99/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8885 - lr: 1.0000e-04\n",
            "Epoch 100/200\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.9054 - lr: 1.0000e-04\n",
            "Epoch 101/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8814 - lr: 1.0000e-04\n",
            "Epoch 102/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.9138 - lr: 1.0000e-04\n",
            "Epoch 103/200\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.9091 - lr: 1.0000e-04\n",
            "Epoch 104/200\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.8882 - lr: 1.0000e-04\n",
            "Epoch 105/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8830 - lr: 1.0000e-04\n",
            "Epoch 106/200\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.8831 - lr: 1.0000e-04\n",
            "Epoch 107/200\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.8736 - lr: 1.0000e-04\n",
            "Epoch 108/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8694 - lr: 1.0000e-04\n",
            "Epoch 109/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8806 - lr: 1.0000e-04\n",
            "Epoch 110/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8800 - lr: 1.0000e-04\n",
            "Epoch 111/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8735 - lr: 1.0000e-04\n",
            "Epoch 112/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8643 - lr: 1.0000e-04\n",
            "Epoch 113/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.9130 - lr: 1.0000e-04\n",
            "Epoch 114/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.9233 - lr: 1.0000e-04\n",
            "Epoch 115/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8751 - lr: 1.0000e-04\n",
            "Epoch 116/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8771 - lr: 1.0000e-04\n",
            "Epoch 117/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8805 - lr: 1.0000e-04\n",
            "Epoch 118/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8764 - lr: 1.0000e-04\n",
            "Epoch 119/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8708 - lr: 1.0000e-04\n",
            "Epoch 120/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8714 - lr: 1.0000e-04\n",
            "Epoch 121/200\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.8702 - lr: 1.0000e-04\n",
            "Epoch 122/200\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.8710 - lr: 1.0000e-04\n",
            "Epoch 123/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8704 - lr: 1.0000e-04\n",
            "Epoch 124/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8715 - lr: 1.0000e-04\n",
            "Epoch 125/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8763 - lr: 1.0000e-04\n",
            "Epoch 126/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8726 - lr: 1.0000e-04\n",
            "Epoch 127/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8789 - lr: 1.0000e-04\n",
            "Epoch 128/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8782 - lr: 1.0000e-04\n",
            "Epoch 129/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8827 - lr: 1.0000e-04\n",
            "Epoch 130/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8928 - lr: 1.0000e-04\n",
            "Epoch 131/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8867 - lr: 1.0000e-04\n",
            "Epoch 132/200\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.8749 - lr: 1.0000e-04\n",
            "Epoch 133/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8723 - lr: 1.0000e-04\n",
            "Epoch 134/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8724 - lr: 1.0000e-04\n",
            "Epoch 135/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8709 - lr: 1.0000e-04\n",
            "Epoch 136/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8705 - lr: 1.0000e-04\n",
            "Epoch 137/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8707 - lr: 1.0000e-04\n",
            "Epoch 138/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8706 - lr: 1.0000e-04\n",
            "Epoch 139/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8768 - lr: 1.0000e-04\n",
            "Epoch 140/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8954 - lr: 1.0000e-04\n",
            "Epoch 141/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8751 - lr: 1.0000e-04\n",
            "Epoch 142/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8817 - lr: 1.0000e-04\n",
            "Epoch 143/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8746 - lr: 1.0000e-04\n",
            "Epoch 144/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8823 - lr: 1.0000e-04\n",
            "Epoch 145/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.9091 - lr: 1.0000e-04\n",
            "Epoch 146/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8936 - lr: 1.0000e-04\n",
            "Epoch 147/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8747 - lr: 1.0000e-04\n",
            "Epoch 148/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8707 - lr: 1.0000e-04\n",
            "Epoch 149/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8735 - lr: 1.0000e-04\n",
            "Epoch 150/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8715 - lr: 1.0000e-04\n",
            "Epoch 151/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8727 - lr: 1.0000e-04\n",
            "Epoch 152/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8883 - lr: 1.0000e-04\n",
            "Epoch 153/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.9035 - lr: 1.0000e-04\n",
            "Epoch 154/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8764 - lr: 1.0000e-04\n",
            "Epoch 155/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.9168 - lr: 1.0000e-04\n",
            "Epoch 156/200\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.9737 - lr: 1.0000e-04\n",
            "Epoch 157/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.9347 - lr: 1.0000e-04\n",
            "Epoch 158/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8641 - lr: 1.0000e-04\n",
            "Epoch 159/200\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.8838 - lr: 1.0000e-04\n",
            "Epoch 160/200\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.9009 - lr: 1.0000e-04\n",
            "Epoch 161/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.8755 - lr: 1.0000e-04\n",
            "Epoch 162/200\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.8824 - lr: 1.0000e-04\n",
            "Epoch 163/200\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.8771 - lr: 1.0000e-04\n",
            "Epoch 164/200\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.8711 - lr: 1.0000e-04\n",
            "Epoch 165/200\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.8698 - lr: 1.0000e-04\n",
            "Epoch 166/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8746 - lr: 1.0000e-04\n",
            "Epoch 167/200\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.8772 - lr: 1.0000e-04\n",
            "Epoch 168/200\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.8757 - lr: 1.0000e-04\n",
            "Epoch 169/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8745 - lr: 1.0000e-04\n",
            "Epoch 170/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8896 - lr: 1.0000e-04\n",
            "Epoch 171/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.9040 - lr: 1.0000e-04\n",
            "Epoch 172/200\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.9098 - lr: 1.0000e-04\n",
            "Epoch 173/200\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.8813 - lr: 1.0000e-04\n",
            "Epoch 174/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8795 - lr: 1.0000e-04\n",
            "Epoch 175/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.9466 - lr: 1.0000e-04\n",
            "Epoch 176/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.9206 - lr: 1.0000e-04\n",
            "Epoch 177/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8777 - lr: 1.0000e-04\n",
            "Epoch 178/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8711 - lr: 1.0000e-04\n",
            "Epoch 179/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8733 - lr: 1.0000e-04\n",
            "Epoch 180/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8800 - lr: 1.0000e-04\n",
            "Epoch 181/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.9136 - lr: 1.0000e-04\n",
            "Epoch 182/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8917 - lr: 1.0000e-04\n",
            "Epoch 183/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8909 - lr: 1.0000e-04\n",
            "Epoch 184/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8852 - lr: 1.0000e-04\n",
            "Epoch 185/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8748 - lr: 1.0000e-04\n",
            "Epoch 186/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8811 - lr: 1.0000e-04\n",
            "Epoch 187/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8776 - lr: 1.0000e-04\n",
            "Epoch 188/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8951 - lr: 1.0000e-04\n",
            "Epoch 189/200\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.9164 - lr: 1.0000e-04\n",
            "Epoch 190/200\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.8726 - lr: 1.0000e-04\n",
            "Epoch 191/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8751 - lr: 1.0000e-04\n",
            "Epoch 192/200\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.8752 - lr: 1.0000e-04\n",
            "Epoch 193/200\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.8724 - lr: 1.0000e-04\n",
            "Epoch 194/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8712 - lr: 1.0000e-04\n",
            "Epoch 195/200\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.8737 - lr: 1.0000e-04\n",
            "Epoch 196/200\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.8739 - lr: 1.0000e-04\n",
            "Epoch 197/200\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.8721 - lr: 1.0000e-04\n",
            "Epoch 198/200\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.8758 - lr: 1.0000e-04\n",
            "Epoch 199/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8910 - lr: 1.0000e-04\n",
            "Epoch 200/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8819 - lr: 1.0000e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot loss"
      ],
      "metadata": {
        "id": "qhrm0mlMXHvt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(r.history['loss'], label='loss')\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "t5jhp5kgXSCK",
        "outputId": "2e9396ad-60ac-4acd-e088-c639901883cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aiPnT9c7Xiqi",
        "outputId": "f8d1fd8b-25d9-43b7-bef4-be415e75177d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.src.layers.core.dense.Dense at 0x7faec8bfbb80>]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers[0].get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "neqo8d81X1bP",
        "outputId": "491e1ce4-e377-4ecd-aa58-23ff745d6d24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.34457794]], dtype=float32), array([17.769896], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = model.layers[0].get_weights()[0][0,0]\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vfoGNqYMX6ce",
        "outputId": "d061f6ec-2aad-4686-c71c-cafe882f9bd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.34457794"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make sure the line fits the data\n",
        "Yhat = model.predict(X).flatten()\n",
        "plt.scatter(X,Y)\n",
        "plt.plot(X, Yhat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "4JF9Ow_7YjN3",
        "outputId": "f3de0389-4d7a-4615-d0d8-c94256d01a04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fae4849d930>]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# manual calculation\n",
        "\n",
        "# get weights\n",
        "w, b = model.layers[0].get_weights()\n",
        "\n",
        "#Reshape X\n",
        "X = X.reshape(-1,1)\n",
        "\n",
        "# (Nx1) * (1x1) +(1) --> (N x 1) so need to flatten\n",
        "Yhat2 = (X.dot(w) + b).flatten()\n",
        "\n",
        "# dont use == for floating point\n",
        "np.allclose(Yhat, Yhat2)"
      ],
      "metadata": {
        "id": "T6EU9ZTYFIZu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8bdb562-f82d-4742-bc22-ff6eed5884ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    }
  ]
}