LinearRegression.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNh0e6nS8zyeSAxqeRldjpm",
      "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/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": "5VA5Wj4OGlmM",
        "colab_type": "code",
        "outputId": "bb29a8cc-3df6-4bce-863f-a92bed24ffe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "#Defining Data Set\n",
        "x_train = [1, 2, 3]\n",
        "y_train = [1, 2, 3]\n",
        "\n",
        "#Defining Weight and Bias\n",
        "W = tf.Variable(tf.random_normal([1]), name = 'weight')\n",
        "b = tf.Variable(tf.random_normal([1]), name = 'bias')\n",
        "\n",
        "#Defining hypothesis\n",
        "hypothesis = x_train * W + b \n",
        "\n",
        "#Cost(Loss) function\n",
        "cost = tf.reduce_mean(tf.square(hypothesis - y_train))\n",
        "\n",
        "#optimizer\n",
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.01)\n",
        "train = optimizer.minimize(cost)\n",
        "\n",
        "#Launch the graph in a session\n",
        "sess = tf.Session()\n",
        "\n",
        "#Initialize the global variable\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "#Run\n",
        "for step in range(2001) :\n",
        "  if step % 20 == 0 :\n",
        "    print(step, sess.run(cost), sess.run(W), sess.run(b))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "60 3.666337 [0.0419951] [0.16830045]\n",
            "80 3.666337 [0.0419951] [0.16830045]\n",
            "100 3.666337 [0.0419951] [0.16830045]\n",
            "120 3.666337 [0.0419951] [0.16830045]\n",
            "140 3.666337 [0.0419951] [0.16830045]\n",
            "160 3.666337 [0.0419951] [0.16830045]\n",
            "180 3.666337 [0.0419951] [0.16830045]\n",
            "200 3.666337 [0.0419951] [0.16830045]\n",
            "220 3.666337 [0.0419951] [0.16830045]\n",
            "240 3.666337 [0.0419951] [0.16830045]\n",
            "260 3.666337 [0.0419951] [0.16830045]\n",
            "280 3.666337 [0.0419951] [0.16830045]\n",
            "300 3.666337 [0.0419951] [0.16830045]\n",
            "320 3.666337 [0.0419951] [0.16830045]\n",
            "340 3.666337 [0.0419951] [0.16830045]\n",
            "360 3.666337 [0.0419951] [0.16830045]\n",
            "380 3.666337 [0.0419951] [0.16830045]\n",
            "400 3.666337 [0.0419951] [0.16830045]\n",
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            "560 3.666337 [0.0419951] [0.16830045]\n",
            "580 3.666337 [0.0419951] [0.16830045]\n",
            "600 3.666337 [0.0419951] [0.16830045]\n",
            "620 3.666337 [0.0419951] [0.16830045]\n",
            "640 3.666337 [0.0419951] [0.16830045]\n",
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            "700 3.666337 [0.0419951] [0.16830045]\n",
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            "900 3.666337 [0.0419951] [0.16830045]\n",
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            "1000 3.666337 [0.0419951] [0.16830045]\n",
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            "1760 3.666337 [0.0419951] [0.16830045]\n",
            "1780 3.666337 [0.0419951] [0.16830045]\n",
            "1800 3.666337 [0.0419951] [0.16830045]\n",
            "1820 3.666337 [0.0419951] [0.16830045]\n",
            "1840 3.666337 [0.0419951] [0.16830045]\n",
            "1860 3.666337 [0.0419951] [0.16830045]\n",
            "1880 3.666337 [0.0419951] [0.16830045]\n",
            "1900 3.666337 [0.0419951] [0.16830045]\n",
            "1920 3.666337 [0.0419951] [0.16830045]\n",
            "1940 3.666337 [0.0419951] [0.16830045]\n",
            "1960 3.666337 [0.0419951] [0.16830045]\n",
            "1980 3.666337 [0.0419951] [0.16830045]\n",
            "2000 3.666337 [0.0419951] [0.16830045]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQRYyNyXAv5z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0724a1d9-d312-4b30-c5dc-641a378eeddc"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "#defining x and y with placeholder\n",
        "x = tf.placeholder(tf.float32, shape = [None])\n",
        "y = tf.placeholder(tf.float32, shape = [None])\n",
        "\n",
        "#defining Weight and Bias\n",
        "W = tf.Variable(tf.random_normal([1]), name = 'weight')\n",
        "b = tf.Variable(tf.random_normal([1]), name = 'bias')\n",
        "\n",
        "#defining hypothesis\n",
        "hypothesis = W * x + b\n",
        "\n",
        "#Cost(Loss) function\n",
        "cost = tf.reduce_mean(tf.square(hypothesis - y))\n",
        "\n",
        "#Optimizer\n",
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.01)\n",
        "train = optimizer.minimize(cost)\n",
        "\n",
        "#Launch the graph in a session\n",
        "sess = tf.Session()\n",
        "\n",
        "#Initialize global_variables in a graph\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "for step in range(2001) : \n",
        "  cost_val, W_val, b_val, _ = sess.run([cost, W, b, train], \n",
        "  feed_dict = {x: [1, 2, 3, 4, 5], y: [2.1, 3.1, 4.1, 5.1, 6.1]})\n",
        "  if step % 20 == 0 :\n",
        "    print(step, cost_val, W_val, b_val)\n",
        "\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "80 0.01530948 [0.9199414] [1.3890368]\n",
            "100 0.0133698955 [0.92518455] [1.3701074]\n",
            "120 0.011675989 [0.93008435] [1.3524175]\n",
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            "860 7.771287e-05 [0.9942961] [1.120593]\n",
            "880 6.7868394e-05 [0.99466956] [1.1192445]\n",
            "900 5.9269893e-05 [0.99501866] [1.117984]\n",
            "920 5.176162e-05 [0.99534494] [1.1168063]\n",
            "940 4.5203255e-05 [0.9956498] [1.1157056]\n",
            "960 3.9474875e-05 [0.99593467] [1.114677]\n",
            "980 3.4475222e-05 [0.9962009] [1.1137159]\n",
            "1000 3.0107703e-05 [0.9964497] [1.1128176]\n",
            "1020 2.6294225e-05 [0.9966822] [1.1119782]\n",
            "1040 2.2961223e-05 [0.99689955] [1.1111935]\n",
            "1060 2.005269e-05 [0.99710256] [1.1104604]\n",
            "1080 1.7510523e-05 [0.99729246] [1.109775]\n",
            "1100 1.5292002e-05 [0.99746984] [1.1091347]\n",
            "1120 1.3354181e-05 [0.9976355] [1.1085365]\n",
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            "1760 1.7510625e-07 [0.9997292] [1.1009774]\n",
            "1780 1.5295338e-07 [0.999747] [1.1009134]\n",
            "1800 1.3360105e-07 [0.9997636] [1.1008536]\n",
            "1820 1.166668e-07 [0.999779] [1.1007977]\n",
            "1840 1.01894884e-07 [0.99979347] [1.1007456]\n",
            "1860 8.898637e-08 [0.99980694] [1.1006968]\n",
            "1880 7.769178e-08 [0.9998196] [1.100651]\n",
            "1900 6.7880094e-08 [0.9998315] [1.1006085]\n",
            "1920 5.926936e-08 [0.99984246] [1.1005687]\n",
            "1940 5.1786458e-08 [0.9998527] [1.1005315]\n",
            "1960 4.5215483e-08 [0.99986243] [1.1004968]\n",
            "1980 3.9445105e-08 [0.99987143] [1.1004641]\n",
            "2000 3.446835e-08 [0.99987984] [1.1004337]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0xDOxnFgnaP",
        "colab_type": "code",
        "outputId": "2bd3b073-2c85-43cb-e6c4-a0f944d5a207",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "a = tf.placeholder(tf.float32)\n",
        "b = tf.placeholder(tf.float32)\n",
        "adder_node = a + b\n",
        "\n",
        "sess = tf.Session()\n",
        "print(sess.run(adder_node, feed_dict = {a : 3.0, b : 4.0}))\n",
        "print(sess.run(adder_node, feed_dict = {a : [3.0, 1.0], b : [4.0, 1.0]}))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7.0\n",
            "[7. 2.]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}