0411_2_normalization.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "0411_2.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "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/iammiori/AI_19_SS_CLASS/blob/master/0411_2_normalization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "0ZLqI79TfIFE",
        "colab_type": "code",
        "outputId": "5067b0ff-690e-45f6-cf1b-6059fd379366",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "\n",
        "if os.path.exists('/content/gdrive')==False:\n",
        "  drive.mount('/content/gdrive')\n",
        "  print('Google Drive is mounted\\n')\n",
        "else:\n",
        "  print('Google Drive is already mounted\\n')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n",
            "Google Drive is mounted\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3T2to6VOfN9G",
        "colab_type": "code",
        "outputId": "5c3bd85d-74ab-4854-bc04-7d085d152ed2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17972
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "#from sklearn.preprocessing import MinMaxScaler\n",
        "# min_max_scalar 를 쓰기 위해 import\n",
        "\n",
        " \n",
        "\n",
        "tf.set_random_seed(777)\n",
        "\n",
        "xy = np.array([[828.659973, 833.450012, 908100, 828.349976, 831.659973],\n",
        "               [823.02002, 828.070007, 1828100, 821.655029, 828.070007],\n",
        "               [819.929993, 824.400024, 1438100, 818.97998, 824.159973],\n",
        "               [816, 820.958984, 1008100, 815.48999, 819.23999],\n",
        "               [819.359985, 823, 1188100, 818.469971, 818.97998],\n",
        "               [819, 823, 1198100, 816, 820.450012],\n",
        "               [811.700012, 815.25, 1098100, 809.780029, 813.669983],\n",
        "               [809.51001, 816.659973, 1398100, 804.539978, 809.559998]])\n",
        "\n",
        "#print(xy)\n",
        "\n",
        "# feature 값의 스케일이 크면 학습이 이루어지지 않고 발산하기 쉬움\n",
        "# min_max_scaler는 최댓값은 1 최소값을 0으로 하여, 그사이에 값들이 있도록 하는 방법이다\n",
        "# 방법은 Xscale = (X-Xmin) / (Xmax-Xmin)\n",
        "\n",
        "\n",
        "def Min_Max_Scaler(xy):\n",
        "    max=np.max(xy)\n",
        "    min=np.min(xy)\n",
        "    scale=(xy-min)/(max-min)\n",
        "    return scale\n",
        "    \n",
        "xy = Min_Max_Scaler(xy)\n",
        "#print(xy)\n",
        "\n",
        "#print(\"xy : \\n{}\\n\".format(xy))\n",
        "\n",
        "# data slicing\n",
        "x_data = xy[:, 0:-1]\n",
        "y_data = xy[:, [-1]]\n",
        "\n",
        "\n",
        "\n",
        "#print('x_data:\\n{}\\n'.format(x_data))\n",
        "\n",
        "# placeholder\n",
        "X = tf.placeholder(tf.float32, shape=[None, 4])\n",
        "Y = tf.placeholder(tf.float32, shape=[None, 1])\n",
        "\n",
        "# weight, bias 설정\n",
        "W = tf.Variable(tf.random_normal([4, 1]), name='weight')\n",
        "b = tf.Variable(tf.random_normal([1]), name='bias')\n",
        "\n",
        "# hypothesis 와 cost 설정\n",
        "hypothesis = tf.matmul(X, W) + b\n",
        "cost = tf.reduce_mean(tf.square(hypothesis - Y))\n",
        "\n",
        "# GradientDescent 최적화\n",
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)\n",
        "train = optimizer.minimize(cost)\n",
        "\n",
        "# session\n",
        "sess = tf.Session()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "for step in range(101):\n",
        "    cost_val, hy_val, _ = sess.run(\n",
        "        [cost, hypothesis, train], feed_dict={X: x_data, Y: y_data})\n",
        "    print(step, \"Cost: \", cost_val, \"\\nPrediction:\\n\", hy_val)\n",
        "\n",
        "                   "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 Cost:  0.072479784 \n",
            "Prediction:\n",
            " [[0.03994584]\n",
            " [0.53036964]\n",
            " [0.3224765 ]\n",
            " [0.09326065]\n",
            " [0.18921113]\n",
            " [0.19454283]\n",
            " [0.1412409 ]\n",
            " [0.30116093]]\n",
            "1 Cost:  0.07247646 \n",
            "Prediction:\n",
            " [[0.03993952]\n",
            " [0.53036153]\n",
            " [0.32246917]\n",
            " [0.09325415]\n",
            " [0.18920428]\n",
            " [0.19453597]\n",
            " [0.14123422]\n",
            " [0.30115366]]\n",
            "2 Cost:  0.07247314 \n",
            "Prediction:\n",
            " [[0.03993323]\n",
            " [0.5303534 ]\n",
            " [0.32246184]\n",
            " [0.09324765]\n",
            " [0.18919742]\n",
            " [0.19452912]\n",
            " [0.14122754]\n",
            " [0.3011464 ]]\n",
            "3 Cost:  0.072469816 \n",
            "Prediction:\n",
            " [[0.03992692]\n",
            " [0.5303453 ]\n",
            " [0.3224545 ]\n",
            " [0.09324116]\n",
            " [0.18919057]\n",
            " [0.19452226]\n",
            " [0.14122087]\n",
            " [0.30113912]]\n",
            "4 Cost:  0.0724665 \n",
            "Prediction:\n",
            " [[0.03992063]\n",
            " [0.5303372 ]\n",
            " [0.32244718]\n",
            " [0.09323466]\n",
            " [0.18918371]\n",
            " [0.19451535]\n",
            " [0.14121419]\n",
            " [0.3011319 ]]\n",
            "5 Cost:  0.07246317 \n",
            "Prediction:\n",
            " [[0.03991431]\n",
            " [0.5303291 ]\n",
            " [0.3224398 ]\n",
            " [0.09322816]\n",
            " [0.18917686]\n",
            " [0.1945085 ]\n",
            " [0.14120752]\n",
            " [0.30112463]]\n",
            "6 Cost:  0.072459854 \n",
            "Prediction:\n",
            " [[0.03990802]\n",
            " [0.530321  ]\n",
            " [0.32243246]\n",
            " [0.09322166]\n",
            " [0.18917   ]\n",
            " [0.19450164]\n",
            " [0.14120084]\n",
            " [0.30111736]]\n",
            "7 Cost:  0.072456524 \n",
            "Prediction:\n",
            " [[0.0399017 ]\n",
            " [0.5303129 ]\n",
            " [0.32242513]\n",
            " [0.09321517]\n",
            " [0.18916315]\n",
            " [0.19449472]\n",
            " [0.14119416]\n",
            " [0.3011101 ]]\n",
            "8 Cost:  0.07245321 \n",
            "Prediction:\n",
            " [[0.03989539]\n",
            " [0.5303048 ]\n",
            " [0.3224178 ]\n",
            " [0.09320867]\n",
            " [0.1891563 ]\n",
            " [0.19448787]\n",
            " [0.14118749]\n",
            " [0.30110282]]\n",
            "9 Cost:  0.072449885 \n",
            "Prediction:\n",
            " [[0.0398891 ]\n",
            " [0.5302967 ]\n",
            " [0.32241046]\n",
            " [0.09320217]\n",
            " [0.18914944]\n",
            " [0.19448102]\n",
            " [0.14118081]\n",
            " [0.30109555]]\n",
            "10 Cost:  0.07244656 \n",
            "Prediction:\n",
            " [[0.03988278]\n",
            " [0.5302886 ]\n",
            " [0.32240307]\n",
            " [0.09319568]\n",
            " [0.18914258]\n",
            " [0.1944741 ]\n",
            " [0.14117414]\n",
            " [0.30108827]]\n",
            "11 Cost:  0.07244325 \n",
            "Prediction:\n",
            " [[0.03987649]\n",
            " [0.5302805 ]\n",
            " [0.32239574]\n",
            " [0.09318912]\n",
            " [0.18913579]\n",
            " [0.19446725]\n",
            " [0.14116746]\n",
            " [0.301081  ]]\n",
            "12 Cost:  0.072439924 \n",
            "Prediction:\n",
            " [[0.03987017]\n",
            " [0.53027236]\n",
            " [0.3223884 ]\n",
            " [0.09318262]\n",
            " [0.18912894]\n",
            " [0.19446039]\n",
            " [0.14116079]\n",
            " [0.30107373]]\n",
            "13 Cost:  0.0724366 \n",
            "Prediction:\n",
            " [[0.03986385]\n",
            " [0.53026426]\n",
            " [0.32238108]\n",
            " [0.09317613]\n",
            " [0.18912208]\n",
            " [0.19445348]\n",
            " [0.14115411]\n",
            " [0.30106652]]\n",
            "14 Cost:  0.072433285 \n",
            "Prediction:\n",
            " [[0.03985757]\n",
            " [0.53025615]\n",
            " [0.32237375]\n",
            " [0.09316963]\n",
            " [0.18911523]\n",
            " [0.19444662]\n",
            " [0.14114743]\n",
            " [0.30105925]]\n",
            "15 Cost:  0.072429955 \n",
            "Prediction:\n",
            " [[0.03985125]\n",
            " [0.53024805]\n",
            " [0.32236636]\n",
            " [0.09316313]\n",
            " [0.18910837]\n",
            " [0.19443977]\n",
            " [0.14114076]\n",
            " [0.30105197]]\n",
            "16 Cost:  0.07242664 \n",
            "Prediction:\n",
            " [[0.03984496]\n",
            " [0.53023994]\n",
            " [0.32235903]\n",
            " [0.09315664]\n",
            " [0.18910152]\n",
            " [0.19443285]\n",
            " [0.14113408]\n",
            " [0.3010447 ]]\n",
            "17 Cost:  0.07242332 \n",
            "Prediction:\n",
            " [[0.03983864]\n",
            " [0.53023183]\n",
            " [0.3223517 ]\n",
            " [0.09315014]\n",
            " [0.18909466]\n",
            " [0.194426  ]\n",
            " [0.14112741]\n",
            " [0.30103743]]\n",
            "18 Cost:  0.07242 \n",
            "Prediction:\n",
            " [[0.03983235]\n",
            " [0.5302237 ]\n",
            " [0.32234436]\n",
            " [0.09314364]\n",
            " [0.18908781]\n",
            " [0.19441915]\n",
            " [0.14112073]\n",
            " [0.30103016]]\n",
            "19 Cost:  0.07241668 \n",
            "Prediction:\n",
            " [[0.03982604]\n",
            " [0.5302156 ]\n",
            " [0.32233703]\n",
            " [0.09313715]\n",
            " [0.18908095]\n",
            " [0.19441229]\n",
            " [0.14111406]\n",
            " [0.3010229 ]]\n",
            "20 Cost:  0.07241335 \n",
            "Prediction:\n",
            " [[0.03981972]\n",
            " [0.5302075 ]\n",
            " [0.32232964]\n",
            " [0.09313065]\n",
            " [0.1890741 ]\n",
            " [0.19440538]\n",
            " [0.14110732]\n",
            " [0.30101562]]\n",
            "21 Cost:  0.07241004 \n",
            "Prediction:\n",
            " [[0.03981343]\n",
            " [0.5301994 ]\n",
            " [0.3223223 ]\n",
            " [0.09312415]\n",
            " [0.18906724]\n",
            " [0.19439852]\n",
            " [0.14110065]\n",
            " [0.3010084 ]]\n",
            "22 Cost:  0.07240671 \n",
            "Prediction:\n",
            " [[0.03980711]\n",
            " [0.5301913 ]\n",
            " [0.32231498]\n",
            " [0.09311765]\n",
            " [0.18906039]\n",
            " [0.19439167]\n",
            " [0.14109397]\n",
            " [0.30100113]]\n",
            "23 Cost:  0.07240339 \n",
            "Prediction:\n",
            " [[0.03980082]\n",
            " [0.5301832 ]\n",
            " [0.32230765]\n",
            " [0.09311116]\n",
            " [0.18905354]\n",
            " [0.19438475]\n",
            " [0.1410873 ]\n",
            " [0.30099386]]\n",
            "24 Cost:  0.07240008 \n",
            "Prediction:\n",
            " [[0.0397945 ]\n",
            " [0.5301751 ]\n",
            " [0.32230031]\n",
            " [0.0931046 ]\n",
            " [0.18904668]\n",
            " [0.1943779 ]\n",
            " [0.14108062]\n",
            " [0.3009866 ]]\n",
            "25 Cost:  0.07239675 \n",
            "Prediction:\n",
            " [[0.03978819]\n",
            " [0.530167  ]\n",
            " [0.32229292]\n",
            " [0.0930981 ]\n",
            " [0.18903983]\n",
            " [0.19437104]\n",
            " [0.14107394]\n",
            " [0.30097932]]\n",
            "26 Cost:  0.07239343 \n",
            "Prediction:\n",
            " [[0.0397819 ]\n",
            " [0.5301589 ]\n",
            " [0.3222856 ]\n",
            " [0.09309161]\n",
            " [0.18903297]\n",
            " [0.19436413]\n",
            " [0.14106727]\n",
            " [0.30097204]]\n",
            "27 Cost:  0.07239011 \n",
            "Prediction:\n",
            " [[0.03977558]\n",
            " [0.5301508 ]\n",
            " [0.32227826]\n",
            " [0.09308511]\n",
            " [0.18902612]\n",
            " [0.19435728]\n",
            " [0.14106059]\n",
            " [0.30096477]]\n",
            "28 Cost:  0.072386794 \n",
            "Prediction:\n",
            " [[0.03976929]\n",
            " [0.53014266]\n",
            " [0.32227093]\n",
            " [0.09307861]\n",
            " [0.18901926]\n",
            " [0.19435042]\n",
            " [0.14105392]\n",
            " [0.3009575 ]]\n",
            "29 Cost:  0.07238347 \n",
            "Prediction:\n",
            " [[0.03976297]\n",
            " [0.53013456]\n",
            " [0.3222636 ]\n",
            " [0.09307212]\n",
            " [0.18901241]\n",
            " [0.1943435 ]\n",
            " [0.14104724]\n",
            " [0.30095023]]\n",
            "30 Cost:  0.072380155 \n",
            "Prediction:\n",
            " [[0.03975669]\n",
            " [0.53012645]\n",
            " [0.3222562 ]\n",
            " [0.09306562]\n",
            " [0.18900555]\n",
            " [0.19433665]\n",
            " [0.14104056]\n",
            " [0.30094302]]\n",
            "31 Cost:  0.07237683 \n",
            "Prediction:\n",
            " [[0.03975037]\n",
            " [0.53011835]\n",
            " [0.32224888]\n",
            " [0.09305912]\n",
            " [0.1889987 ]\n",
            " [0.1943298 ]\n",
            " [0.14103389]\n",
            " [0.30093575]]\n",
            "32 Cost:  0.07237351 \n",
            "Prediction:\n",
            " [[0.03974405]\n",
            " [0.53011024]\n",
            " [0.32224154]\n",
            " [0.09305263]\n",
            " [0.18899184]\n",
            " [0.19432288]\n",
            " [0.14102721]\n",
            " [0.30092847]]\n",
            "33 Cost:  0.0723702 \n",
            "Prediction:\n",
            " [[0.03973776]\n",
            " [0.53010213]\n",
            " [0.3222342 ]\n",
            " [0.09304613]\n",
            " [0.18898499]\n",
            " [0.19431603]\n",
            " [0.14102054]\n",
            " [0.3009212 ]]\n",
            "34 Cost:  0.07236688 \n",
            "Prediction:\n",
            " [[0.03973144]\n",
            " [0.530094  ]\n",
            " [0.32222688]\n",
            " [0.09303963]\n",
            " [0.18897814]\n",
            " [0.19430918]\n",
            " [0.14101386]\n",
            " [0.30091393]]\n",
            "35 Cost:  0.07236355 \n",
            "Prediction:\n",
            " [[0.03972515]\n",
            " [0.5300859 ]\n",
            " [0.3222195 ]\n",
            " [0.09303313]\n",
            " [0.18897128]\n",
            " [0.19430226]\n",
            " [0.14100718]\n",
            " [0.30090666]]\n",
            "36 Cost:  0.07236023 \n",
            "Prediction:\n",
            " [[0.03971884]\n",
            " [0.5300778 ]\n",
            " [0.32221216]\n",
            " [0.09302664]\n",
            " [0.18896443]\n",
            " [0.1942954 ]\n",
            " [0.14100051]\n",
            " [0.3008994 ]]\n",
            "37 Cost:  0.072356924 \n",
            "Prediction:\n",
            " [[0.03971252]\n",
            " [0.5300697 ]\n",
            " [0.32220483]\n",
            " [0.09302014]\n",
            " [0.18895757]\n",
            " [0.19428855]\n",
            " [0.14099383]\n",
            " [0.3008921 ]]\n",
            "38 Cost:  0.0723536 \n",
            "Prediction:\n",
            " [[0.03970623]\n",
            " [0.5300616 ]\n",
            " [0.3221975 ]\n",
            " [0.09301358]\n",
            " [0.18895072]\n",
            " [0.1942817 ]\n",
            " [0.14098716]\n",
            " [0.3008849 ]]\n",
            "39 Cost:  0.072350286 \n",
            "Prediction:\n",
            " [[0.03969991]\n",
            " [0.5300535 ]\n",
            " [0.32219017]\n",
            " [0.09300709]\n",
            " [0.18894386]\n",
            " [0.19427478]\n",
            " [0.14098048]\n",
            " [0.30087763]]\n",
            "40 Cost:  0.07234696 \n",
            "Prediction:\n",
            " [[0.03969362]\n",
            " [0.5300454 ]\n",
            " [0.32218283]\n",
            " [0.09300059]\n",
            " [0.18893701]\n",
            " [0.19426793]\n",
            " [0.1409738 ]\n",
            " [0.30087036]]\n",
            "41 Cost:  0.07234365 \n",
            "Prediction:\n",
            " [[0.03968731]\n",
            " [0.5300373 ]\n",
            " [0.32217544]\n",
            " [0.09299409]\n",
            " [0.18893015]\n",
            " [0.19426107]\n",
            " [0.14096713]\n",
            " [0.3008631 ]]\n",
            "42 Cost:  0.072340325 \n",
            "Prediction:\n",
            " [[0.03968102]\n",
            " [0.5300292 ]\n",
            " [0.3221681 ]\n",
            " [0.0929876 ]\n",
            " [0.1889233 ]\n",
            " [0.19425416]\n",
            " [0.14096045]\n",
            " [0.30085582]]\n",
            "43 Cost:  0.07233701 \n",
            "Prediction:\n",
            " [[0.0396747 ]\n",
            " [0.5300211 ]\n",
            " [0.32216078]\n",
            " [0.0929811 ]\n",
            " [0.18891644]\n",
            " [0.1942473 ]\n",
            " [0.14095378]\n",
            " [0.30084854]]\n",
            "44 Cost:  0.07233369 \n",
            "Prediction:\n",
            " [[0.03966838]\n",
            " [0.53001297]\n",
            " [0.32215345]\n",
            " [0.0929746 ]\n",
            " [0.18890959]\n",
            " [0.19424045]\n",
            " [0.1409471 ]\n",
            " [0.30084127]]\n",
            "45 Cost:  0.07233037 \n",
            "Prediction:\n",
            " [[0.03966209]\n",
            " [0.53000486]\n",
            " [0.32214612]\n",
            " [0.09296811]\n",
            " [0.18890274]\n",
            " [0.19423354]\n",
            " [0.14094043]\n",
            " [0.300834  ]]\n",
            "46 Cost:  0.07232706 \n",
            "Prediction:\n",
            " [[0.03965577]\n",
            " [0.52999675]\n",
            " [0.32213873]\n",
            " [0.09296161]\n",
            " [0.18889588]\n",
            " [0.19422668]\n",
            " [0.14093375]\n",
            " [0.3008268 ]]\n",
            "47 Cost:  0.07232374 \n",
            "Prediction:\n",
            " [[0.03964949]\n",
            " [0.52998865]\n",
            " [0.3221314 ]\n",
            " [0.09295511]\n",
            " [0.18888903]\n",
            " [0.19421983]\n",
            " [0.14092708]\n",
            " [0.30081952]]\n",
            "48 Cost:  0.072320424 \n",
            "Prediction:\n",
            " [[0.03964317]\n",
            " [0.52998054]\n",
            " [0.32212406]\n",
            " [0.09294862]\n",
            " [0.18888217]\n",
            " [0.19421291]\n",
            " [0.1409204 ]\n",
            " [0.30081224]]\n",
            "49 Cost:  0.07231711 \n",
            "Prediction:\n",
            " [[0.03963685]\n",
            " [0.52997243]\n",
            " [0.32211673]\n",
            " [0.09294212]\n",
            " [0.18887532]\n",
            " [0.19420606]\n",
            " [0.14091372]\n",
            " [0.30080497]]\n",
            "50 Cost:  0.072313786 \n",
            "Prediction:\n",
            " [[0.03963056]\n",
            " [0.5299643 ]\n",
            " [0.3221094 ]\n",
            " [0.09293562]\n",
            " [0.18886846]\n",
            " [0.1941992 ]\n",
            " [0.14090705]\n",
            " [0.3007977 ]]\n",
            "51 Cost:  0.07231046 \n",
            "Prediction:\n",
            " [[0.03962424]\n",
            " [0.5299562 ]\n",
            " [0.322102  ]\n",
            " [0.09292907]\n",
            " [0.18886161]\n",
            " [0.19419229]\n",
            " [0.14090037]\n",
            " [0.30079043]]\n",
            "52 Cost:  0.07230715 \n",
            "Prediction:\n",
            " [[0.03961796]\n",
            " [0.5299481 ]\n",
            " [0.32209468]\n",
            " [0.09292257]\n",
            " [0.18885475]\n",
            " [0.19418544]\n",
            " [0.1408937 ]\n",
            " [0.30078316]]\n",
            "53 Cost:  0.072303824 \n",
            "Prediction:\n",
            " [[0.03961164]\n",
            " [0.52994   ]\n",
            " [0.32208735]\n",
            " [0.09291607]\n",
            " [0.1888479 ]\n",
            " [0.19417858]\n",
            " [0.14088696]\n",
            " [0.3007759 ]]\n",
            "54 Cost:  0.07230051 \n",
            "Prediction:\n",
            " [[0.03960535]\n",
            " [0.5299319 ]\n",
            " [0.32208002]\n",
            " [0.09290957]\n",
            " [0.18884104]\n",
            " [0.19417173]\n",
            " [0.14088029]\n",
            " [0.3007686 ]]\n",
            "55 Cost:  0.0722972 \n",
            "Prediction:\n",
            " [[0.03959903]\n",
            " [0.5299238 ]\n",
            " [0.32207268]\n",
            " [0.09290308]\n",
            " [0.18883419]\n",
            " [0.19416481]\n",
            " [0.14087361]\n",
            " [0.3007614 ]]\n",
            "56 Cost:  0.0722939 \n",
            "Prediction:\n",
            " [[0.03959274]\n",
            " [0.52991575]\n",
            " [0.32206535]\n",
            " [0.09289664]\n",
            " [0.1888274 ]\n",
            " [0.19415802]\n",
            " [0.140867  ]\n",
            " [0.30075413]]\n",
            "57 Cost:  0.07229061 \n",
            "Prediction:\n",
            " [[0.03958648]\n",
            " [0.5299077 ]\n",
            " [0.32205808]\n",
            " [0.09289014]\n",
            " [0.18882054]\n",
            " [0.19415116]\n",
            " [0.14086038]\n",
            " [0.30074692]]\n",
            "58 Cost:  0.072287306 \n",
            "Prediction:\n",
            " [[0.0395802 ]\n",
            " [0.52989966]\n",
            " [0.32205075]\n",
            " [0.09288371]\n",
            " [0.18881375]\n",
            " [0.19414431]\n",
            " [0.1408537 ]\n",
            " [0.3007397 ]]\n",
            "59 Cost:  0.07228401 \n",
            "Prediction:\n",
            " [[0.03957394]\n",
            " [0.5298916 ]\n",
            " [0.32204348]\n",
            " [0.09287721]\n",
            " [0.18880695]\n",
            " [0.19413751]\n",
            " [0.14084709]\n",
            " [0.3007325 ]]\n",
            "60 Cost:  0.07228072 \n",
            "Prediction:\n",
            " [[0.03956765]\n",
            " [0.52988356]\n",
            " [0.3220362 ]\n",
            " [0.09287077]\n",
            " [0.1888001 ]\n",
            " [0.19413066]\n",
            " [0.14084041]\n",
            " [0.30072528]]\n",
            "61 Cost:  0.07227743 \n",
            "Prediction:\n",
            " [[0.03956136]\n",
            " [0.5298755 ]\n",
            " [0.32202888]\n",
            " [0.09286427]\n",
            " [0.1887933 ]\n",
            " [0.1941238 ]\n",
            " [0.1408338 ]\n",
            " [0.30071807]]\n",
            "62 Cost:  0.07227413 \n",
            "Prediction:\n",
            " [[0.0395551 ]\n",
            " [0.5298675 ]\n",
            " [0.3220216 ]\n",
            " [0.09285784]\n",
            " [0.1887865 ]\n",
            " [0.19411701]\n",
            " [0.14082712]\n",
            " [0.30071086]]\n",
            "63 Cost:  0.07227083 \n",
            "Prediction:\n",
            " [[0.03954881]\n",
            " [0.5298594 ]\n",
            " [0.32201427]\n",
            " [0.09285134]\n",
            " [0.18877965]\n",
            " [0.19411016]\n",
            " [0.1408205 ]\n",
            " [0.30070364]]\n",
            "64 Cost:  0.07226754 \n",
            "Prediction:\n",
            " [[0.03954256]\n",
            " [0.5298514 ]\n",
            " [0.322007  ]\n",
            " [0.09284484]\n",
            " [0.18877286]\n",
            " [0.1941033 ]\n",
            " [0.14081389]\n",
            " [0.30069643]]\n",
            "65 Cost:  0.07226425 \n",
            "Prediction:\n",
            " [[0.03953627]\n",
            " [0.52984333]\n",
            " [0.32199973]\n",
            " [0.09283841]\n",
            " [0.18876606]\n",
            " [0.1940965 ]\n",
            " [0.14080721]\n",
            " [0.30068916]]\n",
            "66 Cost:  0.072260946 \n",
            "Prediction:\n",
            " [[0.03952998]\n",
            " [0.5298353 ]\n",
            " [0.3219924 ]\n",
            " [0.09283191]\n",
            " [0.18875921]\n",
            " [0.19408965]\n",
            " [0.1408006 ]\n",
            " [0.30068195]]\n",
            "67 Cost:  0.07225765 \n",
            "Prediction:\n",
            " [[0.03952372]\n",
            " [0.52982724]\n",
            " [0.32198513]\n",
            " [0.09282547]\n",
            " [0.18875241]\n",
            " [0.1940828 ]\n",
            " [0.14079392]\n",
            " [0.30067474]]\n",
            "68 Cost:  0.07225436 \n",
            "Prediction:\n",
            " [[0.03951743]\n",
            " [0.5298192 ]\n",
            " [0.3219778 ]\n",
            " [0.09281898]\n",
            " [0.18874562]\n",
            " [0.194076  ]\n",
            " [0.1407873 ]\n",
            " [0.30066752]]\n",
            "69 Cost:  0.07225107 \n",
            "Prediction:\n",
            " [[0.03951117]\n",
            " [0.52981114]\n",
            " [0.32197052]\n",
            " [0.09281254]\n",
            " [0.18873876]\n",
            " [0.19406915]\n",
            " [0.14078063]\n",
            " [0.3006603 ]]\n",
            "70 Cost:  0.072247766 \n",
            "Prediction:\n",
            " [[0.03950489]\n",
            " [0.5298031 ]\n",
            " [0.3219632 ]\n",
            " [0.09280604]\n",
            " [0.18873197]\n",
            " [0.1940623 ]\n",
            " [0.14077401]\n",
            " [0.3006531 ]]\n",
            "71 Cost:  0.07224448 \n",
            "Prediction:\n",
            " [[0.03949863]\n",
            " [0.52979505]\n",
            " [0.32195592]\n",
            " [0.0927996 ]\n",
            " [0.18872517]\n",
            " [0.1940555 ]\n",
            " [0.1407674 ]\n",
            " [0.3006459 ]]\n",
            "72 Cost:  0.07224119 \n",
            "Prediction:\n",
            " [[0.03949234]\n",
            " [0.529787  ]\n",
            " [0.32194865]\n",
            " [0.09279311]\n",
            " [0.18871832]\n",
            " [0.19404864]\n",
            " [0.14076072]\n",
            " [0.30063868]]\n",
            "73 Cost:  0.07223789 \n",
            "Prediction:\n",
            " [[0.03948605]\n",
            " [0.52977896]\n",
            " [0.32194132]\n",
            " [0.09278667]\n",
            " [0.18871152]\n",
            " [0.19404179]\n",
            " [0.1407541 ]\n",
            " [0.30063146]]\n",
            "74 Cost:  0.07223459 \n",
            "Prediction:\n",
            " [[0.03947979]\n",
            " [0.5297709 ]\n",
            " [0.32193404]\n",
            " [0.09278017]\n",
            " [0.18870473]\n",
            " [0.194035  ]\n",
            " [0.14074743]\n",
            " [0.3006242 ]]\n",
            "75 Cost:  0.0722313 \n",
            "Prediction:\n",
            " [[0.0394735 ]\n",
            " [0.52976286]\n",
            " [0.3219267 ]\n",
            " [0.09277374]\n",
            " [0.18869787]\n",
            " [0.19402814]\n",
            " [0.14074081]\n",
            " [0.30061698]]\n",
            "76 Cost:  0.072228 \n",
            "Prediction:\n",
            " [[0.03946725]\n",
            " [0.5297548 ]\n",
            " [0.32191944]\n",
            " [0.09276724]\n",
            " [0.18869108]\n",
            " [0.19402128]\n",
            " [0.14073414]\n",
            " [0.30060977]]\n",
            "77 Cost:  0.072224714 \n",
            "Prediction:\n",
            " [[0.03946096]\n",
            " [0.5297468 ]\n",
            " [0.32191217]\n",
            " [0.0927608 ]\n",
            " [0.18868428]\n",
            " [0.19401449]\n",
            " [0.14072752]\n",
            " [0.30060256]]\n",
            "78 Cost:  0.07222141 \n",
            "Prediction:\n",
            " [[0.03945467]\n",
            " [0.5297387 ]\n",
            " [0.32190484]\n",
            " [0.0927543 ]\n",
            " [0.18867743]\n",
            " [0.19400764]\n",
            " [0.1407209 ]\n",
            " [0.30059534]]\n",
            "79 Cost:  0.07221813 \n",
            "Prediction:\n",
            " [[0.03944841]\n",
            " [0.5297307 ]\n",
            " [0.32189757]\n",
            " [0.09274787]\n",
            " [0.18867064]\n",
            " [0.19400078]\n",
            " [0.14071423]\n",
            " [0.30058813]]\n",
            "80 Cost:  0.07221483 \n",
            "Prediction:\n",
            " [[0.03944212]\n",
            " [0.52972263]\n",
            " [0.32189023]\n",
            " [0.09274137]\n",
            " [0.18866384]\n",
            " [0.19399399]\n",
            " [0.14070761]\n",
            " [0.30058092]]\n",
            "81 Cost:  0.072211534 \n",
            "Prediction:\n",
            " [[0.03943586]\n",
            " [0.5297146 ]\n",
            " [0.32188296]\n",
            " [0.09273493]\n",
            " [0.18865699]\n",
            " [0.19398713]\n",
            " [0.14070094]\n",
            " [0.3005737 ]]\n",
            "82 Cost:  0.07220824 \n",
            "Prediction:\n",
            " [[0.03942958]\n",
            " [0.52970654]\n",
            " [0.3218757 ]\n",
            " [0.09272844]\n",
            " [0.18865019]\n",
            " [0.19398028]\n",
            " [0.14069432]\n",
            " [0.30056643]]\n",
            "83 Cost:  0.07220495 \n",
            "Prediction:\n",
            " [[0.03942329]\n",
            " [0.5296985 ]\n",
            " [0.32186836]\n",
            " [0.092722  ]\n",
            " [0.1886434 ]\n",
            " [0.19397348]\n",
            " [0.14068764]\n",
            " [0.30055922]]\n",
            "84 Cost:  0.072201654 \n",
            "Prediction:\n",
            " [[0.03941703]\n",
            " [0.52969044]\n",
            " [0.3218611 ]\n",
            " [0.0927155 ]\n",
            " [0.18863654]\n",
            " [0.19396663]\n",
            " [0.14068103]\n",
            " [0.300552  ]]\n",
            "85 Cost:  0.07219836 \n",
            "Prediction:\n",
            " [[0.03941074]\n",
            " [0.5296824 ]\n",
            " [0.32185376]\n",
            " [0.092709  ]\n",
            " [0.18862975]\n",
            " [0.19395977]\n",
            " [0.14067441]\n",
            " [0.3005448 ]]\n",
            "86 Cost:  0.07219507 \n",
            "Prediction:\n",
            " [[0.03940448]\n",
            " [0.52967435]\n",
            " [0.3218465 ]\n",
            " [0.09270257]\n",
            " [0.18862295]\n",
            " [0.19395298]\n",
            " [0.14066774]\n",
            " [0.3005376 ]]\n",
            "87 Cost:  0.072191775 \n",
            "Prediction:\n",
            " [[0.03939819]\n",
            " [0.5296663 ]\n",
            " [0.3218392 ]\n",
            " [0.09269607]\n",
            " [0.1886161 ]\n",
            " [0.19394612]\n",
            " [0.14066112]\n",
            " [0.30053037]]\n",
            "88 Cost:  0.07218848 \n",
            "Prediction:\n",
            " [[0.03939193]\n",
            " [0.52965826]\n",
            " [0.32183188]\n",
            " [0.09268963]\n",
            " [0.1886093 ]\n",
            " [0.19393927]\n",
            " [0.14065444]\n",
            " [0.30052316]]\n",
            "89 Cost:  0.07218519 \n",
            "Prediction:\n",
            " [[0.03938565]\n",
            " [0.5296502 ]\n",
            " [0.3218246 ]\n",
            " [0.09268314]\n",
            " [0.1886025 ]\n",
            " [0.19393247]\n",
            " [0.14064783]\n",
            " [0.30051595]]\n",
            "90 Cost:  0.072181895 \n",
            "Prediction:\n",
            " [[0.03937936]\n",
            " [0.52964216]\n",
            " [0.32181728]\n",
            " [0.0926767 ]\n",
            " [0.18859565]\n",
            " [0.19392562]\n",
            " [0.14064115]\n",
            " [0.30050874]]\n",
            "91 Cost:  0.0721786 \n",
            "Prediction:\n",
            " [[0.0393731 ]\n",
            " [0.5296341 ]\n",
            " [0.32181   ]\n",
            " [0.0926702 ]\n",
            " [0.18858886]\n",
            " [0.19391882]\n",
            " [0.14063454]\n",
            " [0.30050147]]\n",
            "92 Cost:  0.07217532 \n",
            "Prediction:\n",
            " [[0.03936681]\n",
            " [0.5296261 ]\n",
            " [0.32180274]\n",
            " [0.09266376]\n",
            " [0.18858206]\n",
            " [0.19391197]\n",
            " [0.14062792]\n",
            " [0.30049425]]\n",
            "93 Cost:  0.072172016 \n",
            "Prediction:\n",
            " [[0.03936055]\n",
            " [0.529618  ]\n",
            " [0.3217954 ]\n",
            " [0.09265727]\n",
            " [0.18857521]\n",
            " [0.19390512]\n",
            " [0.14062124]\n",
            " [0.30048704]]\n",
            "94 Cost:  0.07216874 \n",
            "Prediction:\n",
            " [[0.03935426]\n",
            " [0.52961   ]\n",
            " [0.32178813]\n",
            " [0.09265083]\n",
            " [0.18856841]\n",
            " [0.19389832]\n",
            " [0.14061463]\n",
            " [0.30047983]]\n",
            "95 Cost:  0.07216544 \n",
            "Prediction:\n",
            " [[0.03934798]\n",
            " [0.52960193]\n",
            " [0.3217808 ]\n",
            " [0.09264433]\n",
            " [0.18856162]\n",
            " [0.19389147]\n",
            " [0.14060795]\n",
            " [0.30047262]]\n",
            "96 Cost:  0.072162144 \n",
            "Prediction:\n",
            " [[0.03934172]\n",
            " [0.5295939 ]\n",
            " [0.32177353]\n",
            " [0.0926379 ]\n",
            " [0.18855476]\n",
            " [0.19388461]\n",
            " [0.14060134]\n",
            " [0.3004654 ]]\n",
            "97 Cost:  0.07215885 \n",
            "Prediction:\n",
            " [[0.03933543]\n",
            " [0.52958584]\n",
            " [0.3217662 ]\n",
            " [0.0926314 ]\n",
            " [0.18854797]\n",
            " [0.19387782]\n",
            " [0.14059466]\n",
            " [0.3004582 ]]\n",
            "98 Cost:  0.072155565 \n",
            "Prediction:\n",
            " [[0.03932917]\n",
            " [0.5295778 ]\n",
            " [0.32175893]\n",
            " [0.09262496]\n",
            " [0.18854117]\n",
            " [0.19387096]\n",
            " [0.14058805]\n",
            " [0.30045098]]\n",
            "99 Cost:  0.07215227 \n",
            "Prediction:\n",
            " [[0.03932288]\n",
            " [0.52956975]\n",
            " [0.32175165]\n",
            " [0.09261847]\n",
            " [0.18853432]\n",
            " [0.1938641 ]\n",
            " [0.14058143]\n",
            " [0.3004437 ]]\n",
            "100 Cost:  0.07214898 \n",
            "Prediction:\n",
            " [[0.03931659]\n",
            " [0.5295617 ]\n",
            " [0.32174432]\n",
            " [0.09261203]\n",
            " [0.18852752]\n",
            " [0.19385731]\n",
            " [0.14057475]\n",
            " [0.3004365 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NWR0T4VR6wS_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "값이 [0,1]  사이에 있는거 확인 가능하다"
      ]
    }
  ]
}