Tensorflow_simple_Linear_Regression_using_GD.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tensorflow simple Linear Regression using GD.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToVJ-4Lw1nqN"
      },
      "source": [
        "# WRITTEN BY MOHAMMAD ASADOLAHI  \n",
        "# Mohammad.E.Asadolahi@gmail.com  \n",
        "# https://github.com/mohammadAsadolahi  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tmFuJ6l1nFU"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5oN-r7KISOJ"
      },
      "source": [
        "x=np.array([1,2,3,4,5,6,7,8,9,10])\n",
        "y=x*3+7"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYn2rorcINNR"
      },
      "source": [
        "learning_rate=0.001\n",
        "\n",
        "X=tf.constant(tf.cast(x,dtype=tf.float32))\n",
        "X=tf.reshape(X,shape=(1,10))\n",
        "\n",
        "weights=tf.Variable(tf.random.uniform([10,1],minval=-1,maxval=1),dtype=tf.float32)#\n",
        "bias=tf.Variable(tf.random.uniform([1]))\n",
        "\n",
        "def linearRegressor(Xvector):\n",
        "  result=tf.add(tf.reduce_sum(tf.matmul(weights,Xvector),axis=0),bias)\n",
        "  return result\n",
        "\n",
        "def loss(Y_pred,Y_target):\n",
        "  return tf.reduce_mean(tf.square(Y_pred-Y_target))\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fLyu3BxOTXse",
        "outputId": "5e9d154a-4b63-4697-b648-89342b9f0b4d"
      },
      "source": [
        "for epoch in range(4000):\n",
        "  with tf.GradientTape() as tape:\n",
        "    result=linearRegressor(X)\n",
        "    cost=loss(result,y)\n",
        "    gradients = tape.gradient(cost, [weights,bias])\n",
        "    weights.assign_sub(gradients[0]*learning_rate)\n",
        "    bias.assign_sub(gradients[1]*learning_rate)\n",
        "    print(f\"loss of epoch:{epoch}\",cost.numpy())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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