LinearRegression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LinearRegression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOwk4sWdTpyC00VYuyxB5tI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ricnov/Scripts/blob/master/LinearRegression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GiygLnjvN8HR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "83158f14-0f26-4339-d152-97feb1244d06"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x_train = np.linspace(0, 10, 100)\n",
        "\n",
        "y_train = x_train + np.random.normal(0,1,100)\n",
        "\n",
        "plt.scatter(x_train, y_train)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUqrvmM2OBQd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "a44ebb08-8084-4ad5-8f09-cc15a35215e9"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "learning_rate = 0.01\n",
        "\n",
        "# steps of looping through all your data to update the parameters\n",
        "\n",
        "training_epochs = 100\n",
        "\n",
        "# a training set\n",
        "\n",
        "x_train = np.linspace(0, 10, 100)\n",
        "\n",
        "y_train = x_train + np.random.normal(0,1,100)\n",
        "\n",
        "# set up placeholders for input and output\n",
        "\n",
        "X = tf.placeholder(tf.float32)\n",
        "\n",
        "Y = tf.placeholder(tf.float32)\n",
        "\n",
        "# Define h(x) = x*w1 + w0\n",
        "\n",
        "def h(X, w1, w0):\n",
        "\n",
        "       return tf.add(tf.multiply(X, w1), w0)\n",
        "\n",
        "# set up variables for weights\n",
        "\n",
        "w0 = tf.Variable(0.0, name=\"weights\")\n",
        "\n",
        "w1 = tf.Variable(0.0, name=\"weights\")\n",
        "\n",
        "y_predicted = h(X, w1, w0)\n",
        "\n",
        "# Define the cost function\n",
        "\n",
        "costF = 0.5*tf.square(Y-y_predicted)\n",
        "\n",
        "# Define the operation that will be called on each iteration\n",
        "\n",
        "train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(costF)\n",
        "\n",
        "sess = tf.Session()\n",
        "\n",
        "init = tf.global_variables_initializer()\n",
        "\n",
        "sess.run(init)\n",
        "\n",
        "# Loop through the data training\n",
        "\n",
        "for epoch in range(training_epochs):\n",
        "\n",
        "       for (x, y) in zip(x_train, y_train):\n",
        "\n",
        "              sess.run(train_op, feed_dict={X: x, Y: y})\n",
        "\n",
        "# get values of the final weights\n",
        "\n",
        "w_val_0 = sess.run(w0)\n",
        "\n",
        "w_val_1 = sess.run(w1)\n",
        "\n",
        "sess.close()\n",
        "\n",
        "# plot the data training\n",
        "\n",
        "plt.scatter(x_train, y_train)\n",
        "\n",
        "# plot the best fit line\n",
        "\n",
        "y_learned = x_train*w_val_1 + w_val_0\n",
        "\n",
        "plt.plot(x_train, y_learned, 'r')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DPYgESSs7o5Cxs79PP2XtNX9m/9kzKNmnKSNPvpSX2p9M/Xr5vuV+XYpnRUzp\ntCgsYG5Rt1SMOmHZcA8i8dDKzjglOvtLyofAt9/CHXfA44/zs7x6jDjpUsZ06sWP+XsBNa8qTcXi\npWTLhnsQCVLOB/LaAm0iy+8DTwFs2wYPP+xtq/b999C3L6fU7UJpw8bVDvULarE+ZM1E2XAPIkHK\nmfLDSKsoo9lFJ5HZX2CbOzgHEybAYYd5fcG7dPE2d3j8ceq1aB7xLX5BLeYyxgyUDfcgEqScCOR+\nAfv2l5fUGmjjXohDQCmAuXPh+OPhoou8fuAzZ8K0adC+PRB7UMuG5fPZcA8iQUo4tWJmBwD/AzTD\nay8yyjn3UKLnDZLfzNiv3WrlQFtTmWBtaZmEUgCffw433QSTJkHz5vD0096Wa3l7Bu14ql8SXT6f\nCQ9/1QJA5CdB5Mh3AAOccx+a2T7AAjOb6ZxbGsC5AxHrQ7DKgdYvUAK15r/jqRVnwwa4805vb8z8\nfLj9dhgwABo29H1LIkEt1qCs0j+RzJNwIHfOrQXWlv/9ezNbBrQAMiaQ+82MCwvy+XHHrloDbaRA\n2aV4Vq0PQWOaLW/fDo895lWjbNwIV14Jd9zBlPWOkQ/Pq/b+IGbF8QTlVPVeF5HoBVq1YmatgY7A\nvAi/6wv0BTjwwAODvGyt/GbGQ88+HIhvUU60+e9aZ8vOweTJ/PCXAey9+ivead2Rp/r8iXMuPxPW\nu4iBdv5XG5i0oCThWXE8QVmlfyKZJ7BAbmZ7A5OAvzjnvqv6e+fcKGAUeAuCgrpuNGqbGcczkwyk\nBO799720yZw5rGtyIHeeN5TZB/0SzPhg8mLq59eJGGjHz1tVbaf5eGbF8QRllf6JZJ5AArmZ5eMF\n8eecc5ODOGfQgn44Flf+u8JXX8GgQTB+PPz859zz3/0ZdfAp7Kzz04PMmh7GVg3iFeJ5FhBrUE7o\nvkUkKRIuPzQzA8YAy5xz9yc+pHCIqwRu0yYoKoJ27WDKFLjlFlixgifadt8jiNcmz6cNbayz4njq\nsVX6J5J5gpiRdwH6AIvNbFH5azc7514N4NwZLepZflkZjBrlba/2739Dnz7e6syWLYHYH8ae+8sW\ne+TIK16PdVYcb+Mulf6JZJYgqlbmgO9+CrnNOXj5ZW815iefQNeu3qbHxxyzx2HxPIzt1GrfmMsG\nIx2voCwSfup+mCwffujtkfnWW14qZcQI+O1vfXfnSeYim6plhuB9UNSWEsmEhT8i8hN1P0yVVatg\n8GB49llvV55HHoG+fb3FPTVI5sw4njJDLfwRCY+c6LWSEt9/7z28bNsWXnjBW16/YoW31VotQTzZ\n4ikzDKzhl4gknWbklcSVStWs1ywAAAlHSURBVNixw+uDMmSIt+HxRRfB8OHQunVKxhyNeMoMtfBH\nJDxCOyOP1JY20fPV1tK2munT4eij4Zpr4OCD4b33vNrwDAriEF+ZYSJdH0UktUIZyOMKurWIKZWw\neDH06AFnnOFt9jBxIrzzDhx3XNzXT6Z4ar/V81skPEKZWqkt6Catd8ratXDrrTB2rNcb/IEH4P/9\nP6hXL/6bSZFYH6YGuTm0iCRXKAO5X9CtmJnHU2lRYx5582a47z6vhHD7dujXz3uwue++Cd5JZlON\nuUg4hDK14penzTOLu9IiUiqhYR48vG2RV4ly221eKmXZMrj//qwP4iISHqEM5H7520SaSVXNI5/9\n7TLenTiQY24fAAccAHPmwIsvwi9+EcQtiIgEJpSpFb/87cgZn0RVZlfjcvW9NsGNN8Krr3rVJ88/\nDxdc4LsiU0Qk3UIZyME/f1tbi1W/FYt7/buUMyY9CU89BXvvDSNHwnXXQf36yb8ZEZEEhDaQRxJN\npUXVipe9yn7kiv97iZNGToSd270qlCFDvOX1Eaj/iIhkmqwK5FB7pUVFvtzcLnotfZsb3/4fWnxf\nysyDj+PUV/7uNbjyEYb+I/qgEck9WRfIa9O8sICW/3qfwW+NocO6FSxu9gsG9OzPqg7HcWq7PVMw\nkXLwiW48nMouh5n4QSMiwcutQP7pp0ycMYL9Z89gzT5N6H/WX5ly+CnUr5fP8Cjy6H5br0XbfyTZ\ngVY73IvkptwI5N9+C3fcAY8/zv7167P02pu4ttnJrNy8K6o8OngBMc8sYoljtP1Hkh1o1ehKJDdl\ndyD/8Ud4+GG4806vzWzfvjB0KO2bNeOtGt7mF/h2OkdBfl7cW6wlO9Bqh3uR3BTKBUG1cg4mTIBD\nD/Vqwrt08RpdPf44NGtW69v9Al9Fs6l4Nx5OdkdBNboSyU3ZNyN/910YMADee49NhxzGkKtGMLVJ\ne5pPXceNPzaKKuj67aFZeeFQPGo6bxDU6EokNwUSyM3sdOAhIA8Y7ZwrDuK8Mfn8cygq8lrK7r8/\nH952H33K2rG5PGbG8mAxWQExFYFWja5Eck/Cmy+bWR7wKXAqsBr4ALjYObfU7z2Bbr78n/94OfCH\nH/a2VLvpJhgwgC4Pz4uYL25RWMDcom7BXDtJVAsuIpEkc/PlY4EVzrkvyi/0PNAL8A3kgdi+HR57\nzKtG2bgRrrgChg2D5s2B8FZwqBZcRGIVxMPOFsCqSj+vLn8tOZyDyZPh8MOhf3/o1AkWLYIxY3YH\ncQjvVmXa9FhEYpWyqhUz62tm881sfmlpaXwn+de/4KST4NxzvV15pk2DGTOgQ4dqh4a1giOs3yRE\nJH2CSK2UAAdU+rll+Wt7cM6NAkaBlyOP60pbtsCKFfDkk3DllVDXG35NOeWw5ZpVCy4isQoikH8A\nHGJmbfAC+EXA7wI4b3WdO8PKlbDXXrtfqi2nnOmBu6pklyiKSPZJOLXinNsBXAfMAJYBLzjnliR6\nXl+VgjhkX045nh3vRSS3BVJH7px7FXg1iHPFKhtzymH8JiEi6RP6JfphrU4REQlK6AN5WKtTRESC\nEvpeK2GtThERCUroAzkopywiuS30qRURkVynQC4iEnIK5CIiIadALiIScgrkIiIhp0AuIhJyCuQi\nIiGnQC4iEnIK5CIiIRealZ3akFhEJLJQBHJtSCwi4i8UqZVs2zxCRCRIoQjk2bh5hIhIUEIRyLV5\nhIiIv1AEcm0eISLiLxQPO7V5hIiIv1AEctDmESIifhJKrZjZSDNbbmb/MrN/mFlhUAMTEZHoJJoj\nnwkc4ZzrAHwKDEp8SCIiEouEArlz7nXn3I7yH98DWiY+JBERiUWQVStXAq/5/dLM+prZfDObX1pa\nGuBlRURyW60PO83sDWC/CL8a7Jx7qfyYwcAO4Dm/8zjnRgGjADp16uTiGq2IiFRjziUWU83scuAa\noLtzbkuU7ykFvorzkk2Ab+N8b1jpnnNDrt1zrt0vJH7PrZxzTau+mFAgN7PTgfuBk51zKcmXmNl8\n51ynVFwrU+iec0Ou3XOu3S8k754TzZE/AuwDzDSzRWb2RABjEhGRGCS0IMg5d3BQAxERkfiEotdK\nFaPSPYA00D3nhly751y7X0jSPSf8sFNERNIrjDNyERGpRIFcRCTkQhXIzex0M/vEzFaYWVG6x5Ns\nZnaAmb1lZkvNbImZ9Uv3mFLBzPLMbKGZvZLusaSCmRWa2cTyBnTLzOz4dI8p2cysf/l/0x+b2Xgz\nq5/uMQXNzJ42s2/M7ONKr+1rZjPN7LPyPxsHca3QBHIzywMeBc4A2gMXm1n79I4q6XYAA5xz7YHO\nwLU5cM8A/YBl6R5ECj0ETHfOHQocRZbfu5m1AP4MdHLOHQHkAReld1RJ8QxwepXXioA3nXOHAG+W\n/5yw0ARy4FhghXPuC+fcduB5oFeax5RUzrm1zrkPy//+Pd7/4FndlN3MWgJnAaPTPZZUMLNGwEnA\nGADn3Hbn3Mb0jiol6gIFZlYXaACsSfN4Auec+yewocrLvYC/l//970DvIK4VpkDeAlhV6efVZHlQ\nq8zMWgMdgXnpHUnSPQgMBHaleyAp0gYoBcaWp5NGm1nDdA8qmZxzJcC9wNfAWmCTc+719I4qZZo5\n59aW/30d0CyIk4YpkOcsM9sbmAT8xTn3XbrHkyxm1hP4xjm3IN1jSaG6wDHA4865jsBmAvq6nanK\n88K98D7EmgMNzez36R1V6jmv9juQ+u8wBfIS4IBKP7csfy2rmVk+XhB/zjk3Od3jSbIuwNlmthIv\nddbNzMald0hJtxpY7Zyr+KY1ES+wZ7PfAF8650qdc2XAZOCENI8pVdab2f4A5X9+E8RJwxTIPwAO\nMbM2ZlYP7+HI1DSPKanMzPByp8ucc/enezzJ5pwb5Jxr6Zxrjffvd5ZzLqtnas65dcAqM2tX/lJ3\nYGkah5QKXwOdzaxB+X/j3cnyB7yVTAUuK//7ZcBLQZw0NJsvO+d2mNl1wAy8p9xPO+eWpHlYydYF\n6AMsNrNF5a/d7Jx7NY1jkuBdDzxXPkH5ArgizeNJKufcPDObCHyIV5m1kCxcrm9m44FTgCZmthq4\nDSgGXjCzq/BaeV8QyLW0RF9EJNzClFoREZEIFMhFREJOgVxEJOQUyEVEQk6BXEQk5BTIRURCToFc\nRCTk/j+Qa/yiLFtJRAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}