TensorflowBeginnersModules.ipynb

{
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  "metadata": {
    "colab": {
      "name": "TensorflowBeginnersModules.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPXDs1+4R7y0vTPHTrbJp7O",
      "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/Atul-Kashyap/AI-Model-Compression/blob/master/TensorflowBeginnersModules.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_9-Ckrkl2Z4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "ee24fb00-9649-4c69-950d-e7a5e52cc520"
      },
      "source": [
        "import tensorflow.compat.v1 as tf\n",
        "tf.disable_v2_behavior()\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUeVjVDIr0zK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hello = tf.constant(\"Hello\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZYmymsG68CU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "world = tf.constant(\"World\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8Q0PN_q7Gdp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "e8ce86bb-2136-4cc2-9b37-68191610cd8c"
      },
      "source": [
        "# hello is tensor object\n",
        "type(hello)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensorflow.python.framework.ops.Tensor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHfnyfAd7Ukc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "08374607-1544-4bba-e7cb-85016c0098de"
      },
      "source": [
        "print(hello)\n",
        "# hello is a constant and tensor object of string data type"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"Const:0\", shape=(), dtype=string)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTZemGZl7XGg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create a tensorflow session \n",
        "with tf.Session() as sess:\n",
        "  result = sess.run(hello+world)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClUUNq-s8b9_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "22ea891f-8f4a-48cc-b5f4-99fdc19f3a54"
      },
      "source": [
        "print(result)\n",
        "# b represents bytes literals"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b'HelloWorld'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlG9jUr-9w2w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = tf.constant(10)\n",
        "b = tf.constant(20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7jCHbVT-akX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "fbc1b1c2-35c3-4115-a071-2d57326a5bd6"
      },
      "source": [
        "type(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensorflow.python.framework.ops.Tensor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Re3wahGJ-ceH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "ff3b2be4-c7a6-4cd9-d214-62b27d61253e"
      },
      "source": [
        "a + b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'add_1:0' shape=() dtype=int32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYIjal0F-ezm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "60368b15-aecb-412e-a2ae-099a41c5bb37"
      },
      "source": [
        "a + b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'add_2:0' shape=() dtype=int32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AFRxtFU-hpw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "2c1cea57-a6a4-41f0-8ec6-705df47fe05d"
      },
      "source": [
        "print(a+b)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"add_3:0\", shape=(), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVvTQbq0-ond",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with tf.Session() as sess:\n",
        "  result = sess.run(a+b)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DngyVq9R-4rh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "62df5e69-a54d-43e3-d339-8440af341696"
      },
      "source": [
        "print(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vHJCLzN_CUz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "const = tf.constant(10)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcSnOPy-_vXg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fill_mat = tf.fill((4,4),10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-cuCqJ5_7S6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "myzero = tf.zeros((4,4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hj8PWJM__91",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "myone = tf.ones((4,4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWWr5deWAF-Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "myrandn = tf.random_normal((4,4), mean = 0, stddev = 1.0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbeI3qqKARDy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "myrandu = tf.random_uniform((4,4),minval = 0, maxval = 10000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJLq-Kn0Ak0N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "91296c4b-dcc0-408e-ea98-644e1fd0143c"
      },
      "source": [
        "myzero"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'zeros:0' shape=(4, 4) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wudHldO9ApCc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_ops = [const,myzero,myone,myrandn, myrandu, myzero]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQw29grpBGVv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sess = tf.InteractiveSession()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRSqYGAkBWk_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "7fd10c9d-8cf3-46c8-e477-a73a0ed9b312"
      },
      "source": [
        "for op in my_ops:\n",
        "  print(sess.run(op))\n",
        "  print(\"\\n\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n",
            "\n",
            "\n",
            "[[0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]]\n",
            "\n",
            "\n",
            "[[1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]]\n",
            "\n",
            "\n",
            "[[ 1.1402931  -0.8264116   0.60550135  0.22908294]\n",
            " [-0.15683357  0.8566015  -0.9515071  -1.3839812 ]\n",
            " [-0.11896003 -0.16616821  0.6932524  -0.7283413 ]\n",
            " [ 2.1570485  -0.5934617   0.25951055 -0.9892822 ]]\n",
            "\n",
            "\n",
            "[[7400.0786 9148.664  9172.256  8120.884 ]\n",
            " [6467.5713 7758.726  4518.0894 1227.5111]\n",
            " [1416.1837 3318.2275 3158.133  4703.1426]\n",
            " [1852.9093 4771.564  9297.385  2181.17  ]]\n",
            "\n",
            "\n",
            "[[0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sS9wyFl1Bibh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = tf.constant([ [1,2],\n",
        "                 [3,4] ])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loXPomWxCYh_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "d720df06-4284-4933-9112-4dfc54dbf312"
      },
      "source": [
        "a.get_shape()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([Dimension(2), Dimension(2)])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8PSMtQhCbdh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b = tf.constant([ [10], [100] ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5SlZwKKBCi3z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "01bc30df-6309-4bff-d113-e4b95813162e"
      },
      "source": [
        "b.get_shape()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([Dimension(2), Dimension(1)])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3KOoLtaCk5p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result = tf.matmul(a,b)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GDTMlTqtCqel",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "0df389fa-317a-4a5f-aee8-2ac18311cc2a"
      },
      "source": [
        "with tf.Session() as sess:\n",
        "  print(sess.run(result))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[210]\n",
            " [430]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvn71jPlC0TV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "6d04ac34-29fb-4b2d-8eb3-a9c9dca1cd9b"
      },
      "source": [
        "result.get_shape()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([Dimension(2), Dimension(1)])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTUEliuGDF7V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "c5d05e67-9cf6-410a-884e-e1795ed9c5bf"
      },
      "source": [
        "result.eval()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[210],\n",
              "       [430]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXagVf_4DNA1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WsDRa4MIBPL",
        "colab_type": "text"
      },
      "source": [
        "Tensorflow Graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BB8djXHRIFwk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n1 = tf.constant(1)\n",
        "n2 = tf.constant(2)\n",
        "n3 = n1 + n2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpDfJSArITij",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with tf.Session() as sess:\n",
        "  result = sess.run(n3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRQ0fAY5IbC-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "01faad35-c9b1-4c61-aa3d-f46ed4b3b258"
      },
      "source": [
        "print(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "en6iKvilIdLh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "03ae9a9b-5f26-44dd-bf33-ba1922429185"
      },
      "source": [
        "print(n3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"add_5:0\", shape=(), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "447EJWDoIgie",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "03ab93e6-1a77-4416-8e20-8f68d55ac992"
      },
      "source": [
        "print(tf.get_default_graph())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.framework.ops.Graph object at 0x7f232463eb70>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4v5IDJ_JVyl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "g = tf.Graph()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-btndbgJZmO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "17171072-f126-4300-b07e-a029fc43d012"
      },
      "source": [
        "print(g)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.framework.ops.Graph object at 0x7f22e24fabe0>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1D2tXHtJdU3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "graph_one = tf.get_default_graph()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNGMjm9hJnhT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "e49d8c9b-ef73-41a1-8178-7e43af538775"
      },
      "source": [
        "print(graph_one)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.framework.ops.Graph object at 0x7f232463eb70>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUS1NnAeJtMf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "graph_two = tf.Graph()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xa4wHq94JzQP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "45b92df7-1305-42f1-8ad2-cc2e8211a40f"
      },
      "source": [
        "print(graph_two)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.framework.ops.Graph object at 0x7f22e2511e48>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9B2jb8QJ189",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "b7cfd8b5-7fd5-45d5-ec6e-f7efcf776323"
      },
      "source": [
        "# there are two graph object graph_one and graph_two \n",
        "# graph_two is now default graph\n",
        "with graph_two.as_default():\n",
        "  print(graph_two is tf.get_default_graph())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OnLScw5KKT_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "d11a4b0a-0d37-41c7-a2c9-e84aa9be71f6"
      },
      "source": [
        "#  but here graph_two is not a default graph\n",
        "print(graph_two is tf.get_default_graph())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnbmM84tKPBD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "2ef1e2e9-33dc-4360-a6e5-03e731b4dc0c"
      },
      "source": [
        "#here graph_one is default graph\n",
        "print(graph_one is tf.get_default_graph())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgwGbC34LNHw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DnGw8nPgLZJN",
        "colab_type": "text"
      },
      "source": [
        "# **Variables and Placeholders**\n",
        "\n",
        "There are two types of tensor objects in a graph:\n",
        " \n",
        "\n",
        "1.   Variables\n",
        "2.   Placeholders\n",
        "\n",
        "\n",
        "\n",
        "Note: \n",
        "* During the optimization process, Tensorflow tunes the parameters of the model \n",
        "* Variables can hold the values of weights and biases throughout the session.\n",
        "Variables need to be initialized.\n",
        "\n",
        "\n",
        "* Placeholders are initially empty and are used to feed in the actual training examples.\n",
        "* However they do need a declared expected data type (tf.float32) with an optional shape argument.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WW0_AB6MQUsR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "351337e4-220d-4e19-a9e4-8e02a1d606b9"
      },
      "source": [
        "sess = tf.InteractiveSession()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py:1751: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIMC1Ut_OB3O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_tensor = tf.random_uniform((4,4),0, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DSf3xlewP6Bt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "0478e0ef-e6cc-4faa-e36d-f97178dd493c"
      },
      "source": [
        "print(my_tensor)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"random_uniform_1/RandomUniform:0\", shape=(4, 4), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6iKyGD90P9CU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_var = tf.Variable(initial_value = my_tensor)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kC_bU2ObQp6G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "65dcb0b9-986c-481a-e0dc-e6b5df74ab0d"
      },
      "source": [
        "print(my_var)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tf.Variable 'Variable:0' shape=(4, 4) dtype=float32_ref>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0AKvqclQ9Np",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#if you try to run the session then it shows error because you need to initialize thr global variables\n",
        "#sess.run(my_var)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaQ94JWdRThg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "init = tf.global_variables_initializer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyVh32NcRi6x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sess.run(init)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KGznrOaRRlzO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "outputId": "7f2ca004-3e91-4b77-e7e8-1216ddbeda21"
      },
      "source": [
        "sess.run(my_var)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.83326936, 0.26152802, 0.499887  , 0.9219732 ],\n",
              "       [0.05140686, 0.55911314, 0.48478568, 0.3999952 ],\n",
              "       [0.55378854, 0.98555326, 0.54222775, 0.7373246 ],\n",
              "       [0.04957652, 0.3958379 , 0.8566761 , 0.25987017]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eF5JAmUiRpk_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ph = tf.placeholder(tf.float32, shape=(None,5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gK5e_R__UOdH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5P5UmeSxgpT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# With the seed reset (every time), the same set of numbers will appear every time.\n",
        "# If the random seed is not reset, different numbers appear with every invocation:\n",
        "np.random.seed(101)\n",
        "tf.set_random_seed(101)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Jwguv60xso2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "17b9a9c7-9211-4577-c285-982919f97e9c"
      },
      "source": [
        "rand_a = np.random.uniform(0,100,(5,5))\n",
        "rand_a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[51.63986277, 57.06675869,  2.84742265, 17.15216562, 68.52769817],\n",
              "       [83.38968626, 30.69662197, 89.36130797, 72.15438618, 18.99389542],\n",
              "       [55.42275911, 35.2131954 , 18.18924027, 78.56017619, 96.54832224],\n",
              "       [23.23536618,  8.35614337, 60.35484223, 72.89927573, 27.62388285],\n",
              "       [68.53063288, 51.78674742,  4.84845374, 13.78692376, 18.69674261]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4U8ZWZ13x9W8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "d9084cac-7ce6-470c-d653-b367c815f60f"
      },
      "source": [
        "rand_b = np.random.uniform(0,100,(5,1))\n",
        "rand_b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[99.43179012],\n",
              "       [52.06653967],\n",
              "       [57.87895355],\n",
              "       [73.48190583],\n",
              "       [54.19617722]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ug-IfCa2yYt0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_features = 10\n",
        "n_dense_neurons = 3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6dTA2I9zX0r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = tf.placeholder(tf.float32,(None,n_features))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8P2wGS6HziQf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "W = tf.Variable(tf.random_normal([n_features,n_dense_neurons]))\n",
        "b = tf.Variable(tf.ones([n_dense_neurons]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HMjnmdsEuiz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xW = tf.matmul(X,W)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQS5oYhfFBER",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z = tf.add(xW,b)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u10if_HAFG6n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = tf.sigmoid(z)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-G55mFfFQMT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "init = tf.global_variables_initializer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x60jt-gQFa5l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with tf.Session() as sess:\n",
        "  sess.run(init)\n",
        "  layer_out = sess.run(a,feed_dict ={X:np.random.random([1,n_features])})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyibHwgPGHAt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "33f70f1f-8ac9-4d68-d400-f6ed2766fe27"
      },
      "source": [
        "print(layer_out)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.82677925 0.05480573 0.8088549 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_GX8PgoGMxO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4NpvjABGmzz",
        "colab_type": "text"
      },
      "source": [
        "# **Simple Linear Regression Example**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyIwIsQeGw-p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_data = np.linspace(0,10,10) + np.random.uniform(-1.5,1.5,10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Nf22GALG7Yq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "4c9498ec-0ccf-49d4-bae9-511a8ba1e89b"
      },
      "source": [
        "x_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.20856056, -0.08034641,  2.82674411,  4.50477294,  3.42312535,\n",
              "        4.88227319,  7.18414126,  6.77068715,  9.4930023 ,  9.96290567])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2YBoqRKHDyn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_data = np.linspace(0,10,10) + np.random.uniform(-1.5,1.5,10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULEy7EysHatH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "9071c424-dfd4-4e86-ddd9-6cfab2c40df1"
      },
      "source": [
        "y_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.5420333 , 1.17575569, 0.85241231, 2.50514314, 4.67005971,\n",
              "       4.41685654, 6.66701681, 6.69180648, 7.54731409, 9.03483077])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-AcWc7NHjQ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RA-m3baIcm5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_data = np.linspace(0.0,10.0,1000000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52jJFsITIux5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "noise = np.random.randn(len(x_data))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCSUgKDpI3Ed",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a9d51218-f0fd-4de1-c094-8853ffbbe48f"
      },
      "source": [
        "noise"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.02598415, -0.1565979 , -0.03157914, ...,  0.49961125,\n",
              "       -0.44382969, -0.39165848])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVbc-jCII4GN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "176c6631-e323-47c7-8e28-0477efa85c05"
      },
      "source": [
        "noise.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNk2G514I8yY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_true = (0.5* x_data) + 5 + noise"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5cVw3PQfJMYl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_df = pd.DataFrame(data = x_data, columns = ['X Data'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2PORkzVJumQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_df = pd.DataFrame(data = y_true, columns = ['Y'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNgDbnaOJ6r_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "788bf9af-562a-42d5-936a-684cb422e2bf"
      },
      "source": [
        "x_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X Data</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.00001</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.00002</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00004</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    X Data\n",
              "0  0.00000\n",
              "1  0.00001\n",
              "2  0.00002\n",
              "3  0.00003\n",
              "4  0.00004"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9v1_REoYKJKE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "outputId": "e1f82f1a-b981-4a27-9818-7bbce52ba466"
      },
      "source": [
        "y_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.025984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.843407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.968431</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.649841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.154866</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Y\n",
              "0  6.025984\n",
              "1  4.843407\n",
              "2  4.968431\n",
              "3  5.649841\n",
              "4  7.154866"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tw_0aKocKMY8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_data = pd.concat([x_df,y_df],axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqx7jalRKXt7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "130f085e-a74f-41ca-9159-4aada74048c0"
      },
      "source": [
        "my_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X Data</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>6.025984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.00001</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.00002</td>\n",
              "      <td>4.968431</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00003</td>\n",
              "      <td>5.649841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00004</td>\n",
              "      <td>7.154866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999995</th>\n",
              "      <td>9.99996</td>\n",
              "      <td>11.589874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999996</th>\n",
              "      <td>9.99997</td>\n",
              "      <td>8.993046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999997</th>\n",
              "      <td>9.99998</td>\n",
              "      <td>10.499601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999998</th>\n",
              "      <td>9.99999</td>\n",
              "      <td>9.556165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999999</th>\n",
              "      <td>10.00000</td>\n",
              "      <td>9.608342</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          X Data          Y\n",
              "0        0.00000   6.025984\n",
              "1        0.00001   4.843407\n",
              "2        0.00002   4.968431\n",
              "3        0.00003   5.649841\n",
              "4        0.00004   7.154866\n",
              "...          ...        ...\n",
              "999995   9.99996  11.589874\n",
              "999996   9.99997   8.993046\n",
              "999997   9.99998  10.499601\n",
              "999998   9.99999   9.556165\n",
              "999999  10.00000   9.608342\n",
              "\n",
              "[1000000 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77TYAMN4KdgM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "db551b71-18f8-4bc8-f571-e2126aa19450"
      },
      "source": [
        "my_data.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f22d95fd4e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SncVWmvVO3fo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "ce7918b8-5804-4bdc-b8f2-48ede0546e02"
      },
      "source": [
        "my_data.sample(n=250).plot(kind = 'scatter',x = 'X Data', y = 'Y')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f22d9561a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoeF_PBzPIYX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 8"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nldWoMOP9Nd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "c24e3ea2-d1cc-4f2a-fdfd-6e8d3877f3ae"
      },
      "source": [
        "np.random.randn(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.0333475 ,  1.67028503])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKAJaogCPqsi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m = tf.Variable(0.5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4U2LMIKcPzMq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b = tf.Variable(1.0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HB6OpphdP3lO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xph = tf.placeholder(tf.float32,[batch_size])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWN1PS1TQNGp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yph = tf.placeholder(tf.float32,[batch_size])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QObcKW3dQSpf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_model = m*xph + b"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiwQdI0oQfQ-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "error = tf.reduce_sum(tf.square(yph-y_model))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3eUDwfsQthY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.001)\n",
        "train = optimizer.minimize(error)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZXx-1b_WUP3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "init = tf.global_variables_initializer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLCkAsrXRH7E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with tf.Session() as sess:\n",
        "  sess.run(init)\n",
        "  batches = 1000\n",
        "  for i in range(batches):\n",
        "    rand_ind = np.random.randint(len(x_data),size = batch_size)\n",
        "    feed = {xph:x_data[rand_ind],yph:y_true[rand_ind]}\n",
        "    sess.run(train,feed_dict = feed)\n",
        "  model_m, model_b = sess.run([m,b])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96jIu_1fResS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "844083b3-e489-4896-80c0-ab3772bd560a"
      },
      "source": [
        "model_m"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4787552"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8cW01u2WjsK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "75e7fb1c-d691-46be-b84b-db578f412741"
      },
      "source": [
        "model_b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.9713974"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wFJqUhrWk5Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_hat = x_data*model_m + model_b"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IaIQzUeWW-M7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "626fc2ba-b400-413d-f825-5502bce6fe91"
      },
      "source": [
        "my_data.sample(250).plot(kind='scatter',x = 'X Data',y = 'Y')\n",
        "plt.plot(x_data,y_hat,'r') "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f22d9074748>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyIQ8ukCXRnJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gMU0qGzDZQqv",
        "colab_type": "text"
      },
      "source": [
        "### **Tensorfow Estimator API**\n",
        "The Estimator types :\n",
        "\n",
        "\n",
        "1.   tf.estimator.LinearClassifier: Construct a linear classification model.\n",
        "2.   tf.estimator.LinearRegressor: Construct a linear Regression model.\n",
        "3.   tf.estimator.DNNClassifier: construct a neural network classification model.\n",
        "3.   tf.estimator.DNNRegressor: construct a neural network regression model.\n",
        "4. in general to use the Estimator API we do the following:\n",
        "\n",
        "\n",
        "   *   Define a list of feature columns.\n",
        "   *   Create the Estimator Model\n",
        "   * Create a Data input Function\n",
        "   * Call train, evaluate, and predict methods on the estimator boject.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3mSX6GbZtBv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feat_cols = [ tf.feature_column.numeric_column('x',shape=[1]) ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8PtqD6BH44F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "23d8785c-6723-4fff-a3e6-427fa39b953c"
      },
      "source": [
        "estimator = tf.estimator.LinearRegressor(feature_columns = feat_cols)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp5r6cd6k7\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp5r6cd6k7', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2R1Y4h_-IcHH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcqeTqn7I-jn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_eval, y_train, y_eval = train_test_split(x_data,y_true,test_size=0.3,random_state=101)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZmkDgM3JSuD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "89fd82f5-f831-4e37-923d-119b4e8f4bad"
      },
      "source": [
        "print(x_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(700000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_lH4y70JV0z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "eed063c6-a222-46c3-d123-3b6a6537e7db"
      },
      "source": [
        "print(x_eval.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(300000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ie5tUxYDJZip",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_func = tf.estimator.inputs.numpy_input_fn({'x':x_train},y_train,batch_size=8,num_epochs = 1000,shuffle= True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4DT9bO26KyFM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_input_func = tf.estimator.inputs.numpy_input_fn({'x':x_train},y_train,batch_size=8,num_epochs = 1000,shuffle= False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGSYAPO8LAqr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "eval_input_func = tf.estimator.inputs.numpy_input_fn({'x':x_eval},y_eval,batch_size=8,num_epochs = 1000,shuffle= False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYgUnzehLOJ0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bafe8199-4f31-446d-a99f-015ba350528b"
      },
      "source": [
        "estimator.train(input_fn=input_func,steps= 1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:65: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:491: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/feature_column/feature_column_v2.py:540: Layer.add_variable (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.add_weight` method instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py:906: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp5r6cd6k7/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
            "INFO:tensorflow:loss = 531.0249, step = 1\n",
            "INFO:tensorflow:global_step/sec: 709.753\n",
            "INFO:tensorflow:loss = 15.789284, step = 101 (0.143 sec)\n",
            "INFO:tensorflow:global_step/sec: 781.154\n",
            "INFO:tensorflow:loss = 24.415295, step = 201 (0.128 sec)\n",
            "INFO:tensorflow:global_step/sec: 779.092\n",
            "INFO:tensorflow:loss = 15.509922, step = 301 (0.129 sec)\n",
            "INFO:tensorflow:global_step/sec: 766.829\n",
            "INFO:tensorflow:loss = 5.86135, step = 401 (0.134 sec)\n",
            "INFO:tensorflow:global_step/sec: 801.276\n",
            "INFO:tensorflow:loss = 22.745934, step = 501 (0.122 sec)\n",
            "INFO:tensorflow:global_step/sec: 823.342\n",
            "INFO:tensorflow:loss = 5.2436404, step = 601 (0.123 sec)\n",
            "INFO:tensorflow:global_step/sec: 769.567\n",
            "INFO:tensorflow:loss = 11.48595, step = 701 (0.128 sec)\n",
            "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 767 vs previous value: 767. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n",
            "INFO:tensorflow:global_step/sec: 683.523\n",
            "INFO:tensorflow:loss = 7.6654496, step = 801 (0.149 sec)\n",
            "INFO:tensorflow:global_step/sec: 773.052\n",
            "INFO:tensorflow:loss = 6.2915606, step = 901 (0.130 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...\n",
            "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmp5r6cd6k7/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...\n",
            "INFO:tensorflow:Loss for final step: 6.295636.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow_estimator.python.estimator.canned.linear.LinearRegressor at 0x7f22d8629f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZV2TtEcLwIO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "f338803e-dcfe-4224-f8a3-1e68bd59cc6f"
      },
      "source": [
        "train_metrics = estimator.evaluate(input_fn = train_input_func, steps = 1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-07-07T12:12:42Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5r6cd6k7/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Evaluation [100/1000]\n",
            "INFO:tensorflow:Evaluation [200/1000]\n",
            "INFO:tensorflow:Evaluation [300/1000]\n",
            "INFO:tensorflow:Evaluation [400/1000]\n",
            "INFO:tensorflow:Evaluation [500/1000]\n",
            "INFO:tensorflow:Evaluation [600/1000]\n",
            "INFO:tensorflow:Evaluation [700/1000]\n",
            "INFO:tensorflow:Evaluation [800/1000]\n",
            "INFO:tensorflow:Evaluation [900/1000]\n",
            "INFO:tensorflow:Evaluation [1000/1000]\n",
            "INFO:tensorflow:Inference Time : 1.31311s\n",
            "INFO:tensorflow:Finished evaluation at 2020-07-07-12:12:44\n",
            "INFO:tensorflow:Saving dict for global step 1000: average_loss = 1.0754042, global_step = 1000, label/mean = 7.49014, loss = 8.603233, prediction/mean = 7.3765507\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: /tmp/tmp5r6cd6k7/model.ckpt-1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xusdoH-JMUZ4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "9a1a61fd-a020-4775-fd14-b6f577521f56"
      },
      "source": [
        "eval_metrics = estimator.evaluate(input_fn = eval_input_func,steps=1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-07-07T12:14:25Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5r6cd6k7/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Evaluation [100/1000]\n",
            "INFO:tensorflow:Evaluation [200/1000]\n",
            "INFO:tensorflow:Evaluation [300/1000]\n",
            "INFO:tensorflow:Evaluation [400/1000]\n",
            "INFO:tensorflow:Evaluation [500/1000]\n",
            "INFO:tensorflow:Evaluation [600/1000]\n",
            "INFO:tensorflow:Evaluation [700/1000]\n",
            "INFO:tensorflow:Evaluation [800/1000]\n",
            "INFO:tensorflow:Evaluation [900/1000]\n",
            "INFO:tensorflow:Evaluation [1000/1000]\n",
            "INFO:tensorflow:Inference Time : 1.30836s\n",
            "INFO:tensorflow:Finished evaluation at 2020-07-07-12:14:26\n",
            "INFO:tensorflow:Saving dict for global step 1000: average_loss = 1.0726013, global_step = 1000, label/mean = 7.4638014, loss = 8.580811, prediction/mean = 7.3555126\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: /tmp/tmp5r6cd6k7/model.ckpt-1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-H4OzPyMthZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "ba61ce19-8239-4d6b-c0d3-2e422cf1ada1"
      },
      "source": [
        "print('Training Data Metrics')\n",
        "print(train_metrics)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Metrics\n",
            "{'average_loss': 1.0754042, 'label/mean': 7.49014, 'loss': 8.603233, 'prediction/mean': 7.3765507, 'global_step': 1000}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_efHTcwIM46n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "2d0b4ce2-1b65-445c-fb4e-1654f1be83be"
      },
      "source": [
        "print(\"Eval Metrics\")\n",
        "print(eval_metrics)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Eval Metrics\n",
            "{'average_loss': 1.0726013, 'label/mean': 7.4638014, 'loss': 8.580811, 'prediction/mean': 7.3555126, 'global_step': 1000}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxfGcwyRNEWC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "brand_new_data = np.linspace(0,10,10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FppaAGnOIMK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_fn_predict  = tf.estimator.inputs.numpy_input_fn({'x':brand_new_data}, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbrOZcCDOg9U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "466b57f6-889f-4bf7-cbbe-586420878ef1"
      },
      "source": [
        "list(estimator.predict(input_fn = input_fn_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5r6cd6k7/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'predictions': array([4.4648294], dtype=float32)},\n",
              " {'predictions': array([5.111586], dtype=float32)},\n",
              " {'predictions': array([5.758343], dtype=float32)},\n",
              " {'predictions': array([6.4051], dtype=float32)},\n",
              " {'predictions': array([7.051857], dtype=float32)},\n",
              " {'predictions': array([7.698613], dtype=float32)},\n",
              " {'predictions': array([8.34537], dtype=float32)},\n",
              " {'predictions': array([8.992126], dtype=float32)},\n",
              " {'predictions': array([9.638884], dtype=float32)},\n",
              " {'predictions': array([10.285641], dtype=float32)}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmkr0k7mOo0g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "5b474eef-ce2a-40fd-aa6b-8cef4bfa4dd0"
      },
      "source": [
        "predictions = []\n",
        "for pred in estimator.predict(input_fn = input_fn_predict):\n",
        "                              predictions.append(pred['predictions'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5r6cd6k7/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bubxAAgaPlcC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "c81031c2-21e5-457e-b34e-b34ea54bc14b"
      },
      "source": [
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([4.4648294], dtype=float32),\n",
              " array([5.111586], dtype=float32),\n",
              " array([5.758343], dtype=float32),\n",
              " array([6.4051], dtype=float32),\n",
              " array([7.051857], dtype=float32),\n",
              " array([7.698613], dtype=float32),\n",
              " array([8.34537], dtype=float32),\n",
              " array([8.992126], dtype=float32),\n",
              " array([9.638884], dtype=float32),\n",
              " array([10.285641], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnLr71q0P-GJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "2c957863-45dc-43b4-fcdd-fcb4e6f3f15a"
      },
      "source": [
        "my_data.sample(n=250).plot(kind='scatter',x='X Data', y = 'Y')\n",
        "plt.plot(brand_new_data,predictions,'r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f22da9ce128>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6az9z7VQV40",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "68b3992d-a818-4f64-ab92-eda5bfda0839"
      },
      "source": [
        "my_data.sample(n=250).plot(kind='scatter',x='X Data', y = 'Y')\n",
        "plt.plot(brand_new_data,predictions,'r*')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f22d04f07f0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSeaea0rQZcJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}