training_and_test_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled10.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN1Rrta1y54XniJqyro0ZGN",
      "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/gaeunfalo16/Deep_Learning/blob/master/training_and_test_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahpf1yo4da2B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "23a65cab-2ba3-4305-c6ed-fc56e0258d95"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "#training data\n",
        "x_data = [[1, 2, 1], [1, 3, 2], [1, 3, 4], [1, 5, 5], [1, 7, 5], [1, 2, 5], [1, 6, 6], [1, 7, 7]]\n",
        "y_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]\n",
        "\n",
        "#test data\n",
        "x_test = [[2, 1, 1], [3, 1, 2], [3, 3, 4]]\n",
        "y_test = [[0, 0, 1], [0, 0, 1], [0, 0, 1]]\n",
        "\n",
        "#placeholder to be fed\n",
        "X = tf.placeholder(\"float\", [None, 3])\n",
        "Y = tf.placeholder(\"float\", [None, 3])\n",
        "\n",
        "#weight and bias\n",
        "W = tf.Variable(tf.random_normal([3, 3]))\n",
        "b = tf.Variable(tf.random_normal([3]))\n",
        "\n",
        "#hypothesis and cost\n",
        "hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)\n",
        "cost = -tf.reduce_mean(tf.reduce_sum(tf.log(hypothesis) * Y, axis = 1))\n",
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.1).minimize(cost)\n",
        "\n",
        "#Correct prediction test model\n",
        "#Select max value from the each row of hypothesis array\n",
        "prediction = tf.arg_max(hypothesis, 1)\n",
        "is_correct = tf.equal(prediction, tf.arg_max(Y, 1))\n",
        "accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n",
        "\n",
        "#Launch the graph\n",
        "with tf.Session() as sess :\n",
        "  #Initialize tensorflow variable\n",
        "  sess.run(tf.global_variables_initializer())\n",
        "  for step in range(201) :\n",
        "    cost_val, W_val, _ = sess.run([cost, W, optimizer], feed_dict = {X: x_data, Y: y_data})\n",
        "    print(step, cost_val, W_val)\n",
        "\n",
        "  #predict\n",
        "  print(\"Prediction : \", sess.run(prediction, feed_dict = {X: x_test}))\n",
        "  #Calculate the accuracy\n",
        "  print(\"Accuracy : \", sess.run(accuracy, feed_dict = {X: x_test, Y: y_test}))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
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            " [ 0.47309044  0.39472985 -0.46371442]]\n",
            "171 0.62875193 [[-1.035188   -0.8326088   1.2725328 ]\n",
            " [ 0.31986716  0.12747245  0.15169862]\n",
            " [ 0.47433653  0.39432105 -0.46455172]]\n",
            "172 0.62784326 [[-1.0398136  -0.8326222   1.2771719 ]\n",
            " [ 0.32043925  0.12808505  0.15051393]\n",
            " [ 0.47558606  0.39391863 -0.46539882]]\n",
            "173 0.6269411 [[-1.0444262  -0.8326298   1.281792  ]\n",
            " [ 0.32100275  0.12868877  0.1493467 ]\n",
            " [ 0.4768389   0.39352247 -0.4662555 ]]\n",
            "174 0.62604505 [[-1.0490259  -0.83263177  1.2863936 ]\n",
            " [ 0.3215578   0.12928377  0.14819664]\n",
            " [ 0.47809497  0.39313236 -0.46712142]]\n",
            "175 0.6251553 [[-1.0536127  -0.83262813  1.2909768 ]\n",
            " [ 0.32210454  0.12987024  0.1470634 ]\n",
            " [ 0.47935423  0.39274806 -0.46799642]]\n",
            "176 0.62427175 [[-1.0581868  -0.83261895  1.2955416 ]\n",
            " [ 0.3226431   0.13044837  0.1459467 ]\n",
            " [ 0.48061663  0.39236945 -0.4688802 ]]\n",
            "177 0.6233942 [[-1.0627481  -0.83260435  1.3000884 ]\n",
            " [ 0.32317352  0.13101839  0.14484623]\n",
            " [ 0.481882    0.39199638 -0.46977252]]\n",
            "178 0.6225226 [[-1.0672969  -0.8325843   1.3046172 ]\n",
            " [ 0.323696    0.13158046  0.14376171]\n",
            " [ 0.48315033  0.39162868 -0.4706731 ]]\n",
            "179 0.6216567 [[-1.0718333  -0.832559    1.3091282 ]\n",
            " [ 0.32421055  0.13213477  0.14269285]\n",
            " [ 0.4844215   0.39126617 -0.47158176]]\n",
            "180 0.62079674 [[-1.0763574  -0.83252835  1.3136216 ]\n",
            " [ 0.32471737  0.13268141  0.14163935]\n",
            " [ 0.48569548  0.39090863 -0.4724982 ]]\n",
            "181 0.6199424 [[-1.0808692  -0.83249253  1.3180977 ]\n",
            " [ 0.3252165   0.13322073  0.14060092]\n",
            " [ 0.48697212  0.39055607 -0.4734223 ]]\n",
            "182 0.6190937 [[-1.085369   -0.8324515   1.3225565 ]\n",
            " [ 0.32570815  0.1337527   0.13957731]\n",
            " [ 0.48825148  0.39020815 -0.47435373]]\n",
            "183 0.61825037 [[-1.0898567  -0.83240545  1.3269982 ]\n",
            " [ 0.3261923   0.13427764  0.13856824]\n",
            " [ 0.48953333  0.38986492 -0.47529235]]\n",
            "184 0.6174126 [[-1.0943326  -0.8323543   1.331423  ]\n",
            " [ 0.32666913  0.13479558  0.13757345]\n",
            " [ 0.49081776  0.38952607 -0.47623792]]\n",
            "185 0.61658025 [[-1.0987967  -0.8322982   1.335831  ]\n",
            " [ 0.3271387   0.1353068   0.13659267]\n",
            " [ 0.49210456  0.38919163 -0.47719026]]\n",
            "186 0.61575305 [[-1.1032491  -0.83223724  1.3402225 ]\n",
            " [ 0.3276012   0.13581128  0.13562566]\n",
            " [ 0.49339378  0.38886127 -0.47814915]]\n",
            "187 0.6149312 [[-1.1076899  -0.8321714   1.3445975 ]\n",
            " [ 0.32805657  0.1363094   0.13467216]\n",
            " [ 0.4946852   0.38853508 -0.47911438]]\n",
            "188 0.6141145 [[-1.1121192  -0.8321007   1.3489561 ]\n",
            " [ 0.32850513  0.13680105  0.13373198]\n",
            " [ 0.49597895  0.38821274 -0.4800858 ]]\n",
            "189 0.61330295 [[-1.1165371  -0.83202523  1.3532985 ]\n",
            " [ 0.32894677  0.13728659  0.1328048 ]\n",
            " [ 0.49727476  0.38789433 -0.4810632 ]]\n",
            "190 0.6124962 [[-1.1209437  -0.8319451   1.357625  ]\n",
            " [ 0.32938176  0.13776594  0.13189045]\n",
            " [ 0.49857277  0.3875795  -0.48204637]]\n",
            "191 0.6116946 [[-1.1253392  -0.8318603   1.3619356 ]\n",
            " [ 0.32981002  0.13823949  0.13098866]\n",
            " [ 0.49987268  0.3872684  -0.48303518]]\n",
            "192 0.6108978 [[-1.1297234  -0.83177096  1.3662306 ]\n",
            " [ 0.33023188  0.13870706  0.13009922]\n",
            " [ 0.5011747   0.38696063 -0.48402944]]\n",
            "193 0.61010575 [[-1.1340967  -0.831677    1.37051   ]\n",
            " [ 0.3306471   0.13916911  0.12922192]\n",
            " [ 0.5024784   0.3866564  -0.48502898]]\n",
            "194 0.6093185 [[-1.1384591  -0.8315786   1.374774  ]\n",
            " [ 0.33105624  0.13962539  0.12835652]\n",
            " [ 0.50378424  0.38635525 -0.48603365]]\n",
            "195 0.6085359 [[-1.1428107  -0.83147573  1.3790227 ]\n",
            " [ 0.33145884  0.14007647  0.12750283]\n",
            " [ 0.50509155  0.38605753 -0.48704326]]\n",
            "196 0.60775805 [[-1.1471515  -0.8313685   1.3832563 ]\n",
            " [ 0.3318556   0.14052191  0.12666063]\n",
            " [ 0.5064009   0.38576257 -0.48805767]]\n",
            "197 0.6069846 [[-1.1514816  -0.83125687  1.3874749 ]\n",
            " [ 0.33224598  0.14096247  0.12582971]\n",
            " [ 0.5077116   0.38547096 -0.48907676]]\n",
            "198 0.6062158 [[-1.1558012  -0.831141    1.3916786 ]\n",
            " [ 0.3326307   0.14139757  0.1250099 ]\n",
            " [ 0.5090242   0.38518187 -0.49010032]]\n",
            "199 0.60545135 [[-1.1601104  -0.83102083  1.3958675 ]\n",
            " [ 0.33300915  0.14182802  0.12420098]\n",
            " [ 0.510338    0.38489595 -0.49112824]]\n",
            "200 0.6046914 [[-1.164409   -0.8308965   1.4000418 ]\n",
            " [ 0.3333822   0.14225316  0.12340277]\n",
            " [ 0.5116537   0.38461235 -0.49216032]]\n",
            "Prediction :  [2 2 2]\n",
            "Accuracy :  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}