DLBook_Project1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DLBook_Project1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1RPeFsXde9EqGaeYoU1X-6O9bt7T5szXF",
      "authorship_tag": "ABX9TyP401u9qRoKY9TjQhrX8aRy",
      "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/Hinni79/deeplearningbook/blob/master/DLBook_Project1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9O0WKJLtHaq",
        "colab_type": "text"
      },
      "source": [
        "# Projekt 1: Lineare Regression\n",
        "## Schritt 1: Datengenerierung und Visualisierung"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WrlP2RnVtaGz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "np.random.seed(1)\n",
        "my_weight = 4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKmRL_U3t8gT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Array mit 100 Werten generieren\n",
        "input = np.arange(0,10,0.1)\n",
        "noise = np.random.randint(low=1, high=10, size=input.shape)\n",
        "output = my_weight * input + noise"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vqt-Kyu--_pV",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOVp1J0kutHW",
        "colab_type": "code",
        "outputId": "96396044-fed4-4863-de61-ad57ad9a1ec2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.scatter(input, output, c=\"red\")\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oIk8QFBPvJEb",
        "colab_type": "text"
      },
      "source": [
        "## Schritt 2: Modell erstellen\n",
        "X wird mit w multipliziert und b addiert"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0qRKQbfvUqV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def linear_regression_model(X, w, b):\n",
        "    return tf.add(tf.multiply(X, w), b)\n",
        "\n",
        "# Platzhalter für X und Y erstellen\n",
        "X = tf.placeholder(tf.float32, name=\"X\")\n",
        "Y = tf.placeholder(tf.float32, name=\"Y\")\n",
        "# Variablen für w und b erstellen\n",
        "w = tf.Variable(0.0, name=\"weights\")\n",
        "b = tf.Variable(0.0, name=\"bias\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XaoVdRk2wjxB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Cost function\n",
        "cost = tf.square(Y - linear_regression_model(X, w, b))\n",
        "train_op = tf.train.GradientDescentOptimizer(0.005).minimize(cost)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrZg2puExtd4",
        "colab_type": "text"
      },
      "source": [
        "## Schritt 3: Modell trainieren\n",
        "For-Schleife über die Anzahl der Epochen, die Trainingsoperation ausführen mit den Trainingswerten. Anschliessend Gewichte und Bias auswerten."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCzDM2ri0Zr5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eba10229-826f-4122-9f28-903a87aa6a04"
      },
      "source": [
        "epochs = range(0,200)\n",
        "with tf.Session() as sess:\n",
        "    sess.run(tf.global_variables_initializer())\n",
        "    for i in epochs:\n",
        "        print(\"--- Epoch {} ---\".format(i))\n",
        "        for (x,y) in zip(input,output):\n",
        "            sess.run(train_op, feed_dict={X:x, Y:y})\n",
        "        \n",
        "        weight_value = sess.run(w)\n",
        "        bias_value = sess.run(b)\n",
        "\n",
        "        print(\"bias_value: {}\".format(bias_value))\n",
        "        print(\"weight_value: {}\".format(weight_value))\n",
        "\n",
        "    input = np.arange(0, 10, 0.1)\n",
        "    predicted_output = sess.run(linear_regression_model(input, weight_value, bias_value))\n",
        "    plt.title('Funktion')\n",
        "    plt.scatter(input, output, c=\"red\", s=4, label=\"Original Werte\")\n",
        "    plt.scatter(input, predicted_output, c=\"g\", s=5, label=\"Vorhersage\")\n",
        "    plt.legend(loc='upper left')\n",
        "    plt.title('Funktion y = x*w + b mit w='+str(weight_value)+' und b='+str(bias_value))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Epoch 0 ---\n",
            "bias_value: 2.5111746788024902\n",
            "weight_value: 3.95035982131958\n",
            "--- Epoch 1 ---\n",
            "bias_value: 2.956328868865967\n",
            "weight_value: 3.9053900241851807\n",
            "--- Epoch 2 ---\n",
            "bias_value: 3.340810537338257\n",
            "weight_value: 3.866549491882324\n",
            "--- Epoch 3 ---\n",
            "bias_value: 3.6728878021240234\n",
            "weight_value: 3.833002805709839\n",
            "--- Epoch 4 ---\n",
            "bias_value: 3.959704637527466\n",
            "weight_value: 3.804028034210205\n",
            "--- Epoch 5 ---\n",
            "bias_value: 4.207431793212891\n",
            "weight_value: 3.7790026664733887\n",
            "--- Epoch 6 ---\n",
            "bias_value: 4.4213948249816895\n",
            "weight_value: 3.7573883533477783\n",
            "--- Epoch 7 ---\n",
            "bias_value: 4.606190204620361\n",
            "weight_value: 3.738720178604126\n",
            "--- Epoch 8 ---\n",
            "bias_value: 4.765801906585693\n",
            "weight_value: 3.722595691680908\n",
            "--- Epoch 9 ---\n",
            "bias_value: 4.903655529022217\n",
            "weight_value: 3.708669424057007\n",
            "--- Epoch 10 ---\n",
            "bias_value: 5.022720813751221\n",
            "weight_value: 3.696641445159912\n",
            "--- Epoch 11 ---\n",
            "bias_value: 5.125558376312256\n",
            "weight_value: 3.6862528324127197\n",
            "--- Epoch 12 ---\n",
            "bias_value: 5.21437931060791\n",
            "weight_value: 3.6772799491882324\n",
            "--- Epoch 13 ---\n",
            "bias_value: 5.291092395782471\n",
            "weight_value: 3.6695306301116943\n",
            "--- Epoch 14 ---\n",
            "bias_value: 5.3573527336120605\n",
            "weight_value: 3.662837266921997\n",
            "--- Epoch 15 ---\n",
            "bias_value: 5.414580821990967\n",
            "weight_value: 3.6570558547973633\n",
            "--- Epoch 16 ---\n",
            "bias_value: 5.4640092849731445\n",
            "weight_value: 3.652062177658081\n",
            "--- Epoch 17 ---\n",
            "bias_value: 5.50670051574707\n",
            "weight_value: 3.647749662399292\n",
            "--- Epoch 18 ---\n",
            "bias_value: 5.543571949005127\n",
            "weight_value: 3.6440250873565674\n",
            "--- Epoch 19 ---\n",
            "bias_value: 5.575417995452881\n",
            "weight_value: 3.640807867050171\n",
            "--- Epoch 20 ---\n",
            "bias_value: 5.602924346923828\n",
            "weight_value: 3.6380293369293213\n",
            "--- Epoch 21 ---\n",
            "bias_value: 5.626681804656982\n",
            "weight_value: 3.6356289386749268\n",
            "--- Epoch 22 ---\n",
            "bias_value: 5.647199630737305\n",
            "weight_value: 3.633556365966797\n",
            "--- Epoch 23 ---\n",
            "bias_value: 5.664923191070557\n",
            "weight_value: 3.6317663192749023\n",
            "--- Epoch 24 ---\n",
            "bias_value: 5.680229663848877\n",
            "weight_value: 3.6302194595336914\n",
            "--- Epoch 25 ---\n",
            "bias_value: 5.693449974060059\n",
            "weight_value: 3.6288840770721436\n",
            "--- Epoch 26 ---\n",
            "bias_value: 5.70487117767334\n",
            "weight_value: 3.62773060798645\n",
            "--- Epoch 27 ---\n",
            "bias_value: 5.714734077453613\n",
            "weight_value: 3.6267337799072266\n",
            "--- Epoch 28 ---\n",
            "bias_value: 5.723252296447754\n",
            "weight_value: 3.625873565673828\n",
            "--- Epoch 29 ---\n",
            "bias_value: 5.730608940124512\n",
            "weight_value: 3.6251301765441895\n",
            "--- Epoch 30 ---\n",
            "bias_value: 5.736963748931885\n",
            "weight_value: 3.624488353729248\n",
            "--- Epoch 31 ---\n",
            "bias_value: 5.7424540519714355\n",
            "weight_value: 3.6239335536956787\n",
            "--- Epoch 32 ---\n",
            "bias_value: 5.747192859649658\n",
            "weight_value: 3.623455047607422\n",
            "--- Epoch 33 ---\n",
            "bias_value: 5.751283168792725\n",
            "weight_value: 3.623041868209839\n",
            "--- Epoch 34 ---\n",
            "bias_value: 5.754820346832275\n",
            "weight_value: 3.6226844787597656\n",
            "--- Epoch 35 ---\n",
            "bias_value: 5.757875919342041\n",
            "weight_value: 3.622375726699829\n",
            "--- Epoch 36 ---\n",
            "bias_value: 5.7605156898498535\n",
            "weight_value: 3.6221091747283936\n",
            "--- Epoch 37 ---\n",
            "bias_value: 5.762792110443115\n",
            "weight_value: 3.6218795776367188\n",
            "--- Epoch 38 ---\n",
            "bias_value: 5.764760971069336\n",
            "weight_value: 3.62168025970459\n",
            "--- Epoch 39 ---\n",
            "bias_value: 5.766460418701172\n",
            "weight_value: 3.6215085983276367\n",
            "--- Epoch 40 ---\n",
            "bias_value: 5.7679266929626465\n",
            "weight_value: 3.6213607788085938\n",
            "--- Epoch 41 ---\n",
            "bias_value: 5.76919412612915\n",
            "weight_value: 3.621232509613037\n",
            "--- Epoch 42 ---\n",
            "bias_value: 5.770290851593018\n",
            "weight_value: 3.621121644973755\n",
            "--- Epoch 43 ---\n",
            "bias_value: 5.771236419677734\n",
            "weight_value: 3.6210262775421143\n",
            "--- Epoch 44 ---\n",
            "bias_value: 5.772054672241211\n",
            "weight_value: 3.620943784713745\n",
            "--- Epoch 45 ---\n",
            "bias_value: 5.77276086807251\n",
            "weight_value: 3.6208724975585938\n",
            "--- Epoch 46 ---\n",
            "bias_value: 5.773370742797852\n",
            "weight_value: 3.6208107471466064\n",
            "--- Epoch 47 ---\n",
            "bias_value: 5.773895740509033\n",
            "weight_value: 3.620757579803467\n",
            "--- Epoch 48 ---\n",
            "bias_value: 5.774348258972168\n",
            "weight_value: 3.6207120418548584\n",
            "--- Epoch 49 ---\n",
            "bias_value: 5.774738788604736\n",
            "weight_value: 3.6206722259521484\n",
            "--- Epoch 50 ---\n",
            "bias_value: 5.775079250335693\n",
            "weight_value: 3.620638132095337\n",
            "--- Epoch 51 ---\n",
            "bias_value: 5.77537202835083\n",
            "weight_value: 3.620608329772949\n",
            "--- Epoch 52 ---\n",
            "bias_value: 5.775625705718994\n",
            "weight_value: 3.6205828189849854\n",
            "--- Epoch 53 ---\n",
            "bias_value: 5.775843620300293\n",
            "weight_value: 3.620561122894287\n",
            "--- Epoch 54 ---\n",
            "bias_value: 5.7760329246521\n",
            "weight_value: 3.620541572570801\n",
            "--- Epoch 55 ---\n",
            "bias_value: 5.776192665100098\n",
            "weight_value: 3.620525598526001\n",
            "--- Epoch 56 ---\n",
            "bias_value: 5.776333332061768\n",
            "weight_value: 3.620511293411255\n",
            "--- Epoch 57 ---\n",
            "bias_value: 5.776456832885742\n",
            "weight_value: 3.6204988956451416\n",
            "--- Epoch 58 ---\n",
            "bias_value: 5.7765607833862305\n",
            "weight_value: 3.6204886436462402\n",
            "--- Epoch 59 ---\n",
            "bias_value: 5.7766523361206055\n",
            "weight_value: 3.620478868484497\n",
            "--- Epoch 60 ---\n",
            "bias_value: 5.776731967926025\n",
            "weight_value: 3.620471239089966\n",
            "--- Epoch 61 ---\n",
            "bias_value: 5.776798725128174\n",
            "weight_value: 3.620464324951172\n",
            "--- Epoch 62 ---\n",
            "bias_value: 5.776857852935791\n",
            "weight_value: 3.6204583644866943\n",
            "--- Epoch 63 ---\n",
            "bias_value: 5.776907920837402\n",
            "weight_value: 3.6204535961151123\n",
            "--- Epoch 64 ---\n",
            "bias_value: 5.776952743530273\n",
            "weight_value: 3.6204488277435303\n",
            "--- Epoch 65 ---\n",
            "bias_value: 5.776991844177246\n",
            "weight_value: 3.6204450130462646\n",
            "--- Epoch 66 ---\n",
            "bias_value: 5.777024269104004\n",
            "weight_value: 3.620441436767578\n",
            "--- Epoch 67 ---\n",
            "bias_value: 5.77705192565918\n",
            "weight_value: 3.620438814163208\n",
            "--- Epoch 68 ---\n",
            "bias_value: 5.777075290679932\n",
            "weight_value: 3.620436429977417\n",
            "--- Epoch 69 ---\n",
            "bias_value: 5.777094841003418\n",
            "weight_value: 3.620434284210205\n",
            "--- Epoch 70 ---\n",
            "bias_value: 5.777112007141113\n",
            "weight_value: 3.6204326152801514\n",
            "--- Epoch 71 ---\n",
            "bias_value: 5.777127742767334\n",
            "weight_value: 3.6204311847686768\n",
            "--- Epoch 72 ---\n",
            "bias_value: 5.7771406173706055\n",
            "weight_value: 3.620429515838623\n",
            "--- Epoch 73 ---\n",
            "bias_value: 5.7771525382995605\n",
            "weight_value: 3.6204283237457275\n",
            "--- Epoch 74 ---\n",
            "bias_value: 5.777162075042725\n",
            "weight_value: 3.6204276084899902\n",
            "--- Epoch 75 ---\n",
            "bias_value: 5.777170658111572\n",
            "weight_value: 3.620426893234253\n",
            "--- Epoch 76 ---\n",
            "bias_value: 5.777177333831787\n",
            "weight_value: 3.6204259395599365\n",
            "--- Epoch 77 ---\n",
            "bias_value: 5.7771830558776855\n",
            "weight_value: 3.620425224304199\n",
            "--- Epoch 78 ---\n",
            "bias_value: 5.777188301086426\n",
            "weight_value: 3.62042498588562\n",
            "--- Epoch 79 ---\n",
            "bias_value: 5.777194023132324\n",
            "weight_value: 3.620424270629883\n",
            "--- Epoch 80 ---\n",
            "bias_value: 5.777198314666748\n",
            "weight_value: 3.6204235553741455\n",
            "--- Epoch 81 ---\n",
            "bias_value: 5.777202129364014\n",
            "weight_value: 3.6204233169555664\n",
            "--- Epoch 82 ---\n",
            "bias_value: 5.777205467224121\n",
            "weight_value: 3.6204235553741455\n",
            "--- Epoch 83 ---\n",
            "bias_value: 5.77720832824707\n",
            "weight_value: 3.6204230785369873\n",
            "--- Epoch 84 ---\n",
            "bias_value: 5.777210235595703\n",
            "weight_value: 3.620422601699829\n",
            "--- Epoch 85 ---\n",
            "bias_value: 5.777213096618652\n",
            "weight_value: 3.620422601699829\n",
            "--- Epoch 86 ---\n",
            "bias_value: 5.777215480804443\n",
            "weight_value: 3.620422124862671\n",
            "--- Epoch 87 ---\n",
            "bias_value: 5.777217388153076\n",
            "weight_value: 3.6204216480255127\n",
            "--- Epoch 88 ---\n",
            "bias_value: 5.777219295501709\n",
            "weight_value: 3.6204216480255127\n",
            "--- Epoch 89 ---\n",
            "bias_value: 5.777220726013184\n",
            "weight_value: 3.6204214096069336\n",
            "--- Epoch 90 ---\n",
            "bias_value: 5.777222633361816\n",
            "weight_value: 3.6204214096069336\n",
            "--- Epoch 91 ---\n",
            "bias_value: 5.777224063873291\n",
            "weight_value: 3.6204214096069336\n",
            "--- Epoch 92 ---\n",
            "bias_value: 5.777225494384766\n",
            "weight_value: 3.6204211711883545\n",
            "--- Epoch 93 ---\n",
            "bias_value: 5.77722692489624\n",
            "weight_value: 3.6204211711883545\n",
            "--- Epoch 94 ---\n",
            "bias_value: 5.777227878570557\n",
            "weight_value: 3.6204209327697754\n",
            "--- Epoch 95 ---\n",
            "bias_value: 5.777228832244873\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 96 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 97 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 98 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 99 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 100 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 101 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 102 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 103 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 104 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 105 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 106 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 107 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 108 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 109 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 110 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 111 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 112 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 113 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 114 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 115 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 116 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 117 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 118 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 119 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 120 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 121 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 122 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 123 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 124 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 125 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 126 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 127 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 128 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 129 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 130 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 131 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 132 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 133 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 134 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 135 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 136 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 137 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 138 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 139 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 140 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 141 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 142 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 143 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 144 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 145 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 146 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 147 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 148 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 149 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 150 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 151 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 152 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 153 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 154 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 155 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 156 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 157 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 158 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 159 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 160 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 161 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 162 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 163 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 164 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 165 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 166 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 167 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 168 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 169 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 170 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 171 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 172 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 173 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 174 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 175 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 176 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 177 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 178 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 179 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 180 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 181 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 182 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 183 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 184 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 185 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 186 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 187 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 188 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 189 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 190 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 191 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 192 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 193 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 194 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 195 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 196 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 197 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 198 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n",
            "--- Epoch 199 ---\n",
            "bias_value: 5.777229309082031\n",
            "weight_value: 3.6204206943511963\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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ruj7VnmSU0jUiklEt3kLdj6qpr1zF2OYBLKz6UGmZNGmevIjkXbvZMpWraKqE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\niJQwBXkRkRKmIC8iUsIU5EVEStj/B6xUThz/9Mf6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCaivqy46oBr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omrl05sR796z",
        "colab_type": "text"
      },
      "source": [
        "## Schritt 4 Modell benutzen und Ergebnisse visualisieren\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVxQ2glV8Eb9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "8b5b4b25-da59-486c-f75e-33753a673048"
      },
      "source": [
        "input = np.arange(0, 10, 0.1)\n",
        "predicted_output = sess.run(linear_regression_model(input, weight_value, bias_value))\n",
        "plt.title('Funktion')\n",
        "plt.scatter(input, output, c=\"red\", s=4, label=\"Original Werte\")\n",
        "plt.scatter(input, predicted_output, c=\"g\", s=5, label=\"Vorhersage\")\n",
        "plt.legend(loc='upper left')\n",
        "plt.title('Funktion y = x*w + b mit w='+str(weight_value)+' und b='+str(bias_value))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-34a58b1ead83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredicted_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinear_regression_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Funktion'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"red\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Original Werte\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"g\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Vorhersage\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    954\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    955\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 956\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    957\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1101\u001b[0m     \u001b[0;31m# Check session.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_closed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1103\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Attempted to use a closed Session.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1105\u001b[0m       raise RuntimeError('The Session graph is empty.  Add operations to the '\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Attempted to use a closed Session."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhSObxJ39EFw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}