machine_learning/Linear_Regression/LinearRegression.ipynb

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    "colab": {
      "name": "LinearRegression.ipynb",
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      "name": "python3",
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      "metadata": {
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          "height": 62
        },
        "outputId": "20672b68-0413-465e-92cb-046f496f2d19"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 1,
      "outputs": [
        {
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          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
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        "outputId": "834a5621-d6d1-4afb-9d7b-bb8085025020"
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      "source": [
        "# creating a node \n",
        "hello = tf.constant(\"hello , tensor\")\n",
        "# creating object\n",
        "sess = tf.Session()\n",
        "\n",
        "print(sess.run(hello))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b'hello , tensor'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRoetMNoaoU_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f0dafdfb-221f-4f24-9ed3-cb8c257b136a"
      },
      "source": [
        "# basic operation\n",
        "\n",
        "# in normal python we write a = 2\n",
        "# but in tensorflow\n",
        "\n",
        "a = tf.constant(2)\n",
        "b = tf.constant(3)\n",
        "\n",
        "with tf.Session() as sess:\n",
        "  print(\"addition of number \", sess.run(a+b))\n",
        "  print(\"multiplication of number \", sess.run(a*b))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "addition of number  5\n",
            "multiplication of number  6\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
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      "source": [
        "# but while using function we have to create placeholder which does define the data type\n",
        "x = tf.placeholder(tf.int32)\n",
        "y = tf.placeholder(tf.int32)\n",
        "\n",
        "add = tf.add(x,y)\n",
        "multi = tf.multiply(x,y)\n",
        "\n",
        "# Launch the default graph.\n",
        "with tf.Session() as sess:\n",
        "  print(\"Addition of number : \", sess.run(add , feed_dict={x:2,y:3}))\n",
        "  print(\"Multiply of number : \", sess.run(multi , feed_dict={x:2,y:3}))\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Addition of number :  5\n",
            "Multiply of number :  6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HV-K6aUGdDZ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5bf8018a-f406-42a5-9362-d4e605c58a9a"
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      "source": [
        "# matrix multiplication\n",
        "\n",
        "#1x2\n",
        "matrix1 = tf.constant([[3., 3.]])\n",
        "\n",
        "# Create another Constant that produces a 2x1 matrix.\n",
        "matrix2 = tf.constant([[2.],[2.]])\n",
        "\n",
        "# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\n",
        "# The returned value, 'product', represents the result of the matrix\n",
        "# multiplication.\n",
        "product = tf.matmul(matrix1, matrix2)\n",
        "\n",
        "with tf.Session() as sess:\n",
        "    result = sess.run(product)\n",
        "    print(result)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[12.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgjAzyOHfoDz",
        "colab_type": "text"
      },
      "source": [
        "#### *_Let's dive into linear regression_*\n",
        "\n",
        "\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fcAVehmHfykd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import library\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "# just use to make graph looks nicer\n",
        "%config InlineBackend.figure_format = 'svg'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Bv2IgSghbR3",
        "colab_type": "text"
      },
      "source": [
        "**linear regression using tensorflow**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_BVwekugOOu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# y = f(x)\n",
        "# we have set of input and set of output based on input dataset\n",
        "# what we have to find out is f(x) which will be the relation between x and y (i.e input and output)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StbI0bxChU-1",
        "colab_type": "text"
      },
      "source": [
        "we need to learn the relationship between them that is called a **hypothesis.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BnLpr-0Ahx82",
        "colab_type": "text"
      },
      "source": [
        "In case of Linear regression, the hypothesis is a straight line, i.e,\n",
        " **h(x) = wx + b**\n",
        "Where w is a vector called **Weights** and b is a scalar called **Bias**. The Weights and Bias are called the parameters of the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EYMqr_z7iWpb",
        "colab_type": "text"
      },
      "source": [
        "All we need to do is estimate the value of **w** and **b** from the given set of data such that the resultant hypothesis produces the **least cost J** which is defined by the following **cost function**\n",
        "  \n",
        " ![image.png](data:image/png;base64,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)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMgc9-b3jAXa",
        "colab_type": "text"
      },
      "source": [
        "where m is the number of data points in the given dataset. This cost function is also called Mean Squared Error."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZnwY7TIhW8N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# let's start coding\n",
        "\n",
        "learning_rate = 0.01\n",
        "epochs = 200\n",
        "\n",
        "n_sample= 30"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-UU3EDUjphJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# now take a random points\n",
        "\n",
        "train_x = np.linspace(0,20,n_sample)\n",
        "# y = mx + c + noise\n",
        "train_y = 3*train_x + 4*np.random.randn(n_sample)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XN1caRv3kAD1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "7ee0b468-1933-48cc-a9f0-172b44375d3a"
      },
      "source": [
        "# let's plot a graph\n",
        "plt.plot(train_x, train_y)\n",
        "plt.show()\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOQPqSBKkYeP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "93889cc8-261b-4eb5-c552-7a0a20ad59eb"
      },
      "source": [
        "# Hmmm i don't like lines\n",
        "# lets make points\n",
        "\n",
        "plt.plot(train_x, train_y , 'o')\n",
        "plt.show()\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btqjEJ07k1m6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "297725fc-906d-4379-bf85-809301433eb1"
      },
      "source": [
        "# bit nicer\n",
        "\n",
        "# lets check difference between lines \n",
        "# with noise and without noise\n",
        "plt.plot(train_x, train_y , 'o')\n",
        "plt.plot(train_x, 3*train_x)\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qqEL7xN3lVKs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d3c6e33b-c270-461a-e5a3-3a41b1632900"
      },
      "source": [
        "# define variable \n",
        "\n",
        "X = tf.placeholder(tf.float32)\n",
        "Y = tf.placeholder(tf.float32)\n",
        "\n",
        "# only written single value\n",
        "w = tf.Variable(np.random.randn() , name = 'weight')\n",
        "b = tf.Variable(np.random.randn() , name = 'bias')\n",
        "print(b.value())"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"bias_4/read:0\", shape=(), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjX82BQRmptu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  # pred = x*w + b\n",
        "  prediction = tf.add(tf.multiply(X, w) ,b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JINE6KURnyTI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# to minimize the cost\n",
        "cost = tf.reduce_sum((prediction-Y)**2 / (2*n_sample))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XC-L2qL8oHkS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define optimizer\n",
        "\n",
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "126byIRwoNrJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# initalize all are parameters\n",
        "init = tf.global_variables_initializer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fVU5skypGcQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "c036a817-2a4f-4948-ffc6-6caef7c8308b"
      },
      "source": [
        "with tf.Session() as sess:\n",
        "  sess.run(init)\n",
        "  # number of procedure will be decided by epoch\n",
        "  for epoch in range(epochs):\n",
        "    for x, y in zip(train_x , train_y):\n",
        "      sess.run(optimizer , feed_dict={X:x , Y:y})\n",
        "      \n",
        "      # let us see output\n",
        "    if (epoch%20) == 0:\n",
        "      c = sess.run(cost, feed_dict={X:train_x ,Y:train_y})\n",
        "      W = sess.run(w)\n",
        "      B = sess.run(b)\n",
        "      print(\"cost:{} w:{} b:{}\".format(c ,W, B))\n",
        "      # we see that cost minimizing\n",
        "      #print(f'epoch:{epoch:04d} c={cost:.4f} W={W:.4f} B={B:.4f}')\n",
        "  weight = sess.run(w)\n",
        "  bias = sess.run(b)   \n",
        "  plt.plot(train_x , train_y ,'o')\n",
        "  plt.plot(train_x ,weight * train_x + bias)\n",
        "  plt.show()\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cost:54.30986404418945 w:2.182626247406006 b:0.5245328545570374\n",
            "cost:8.714800834655762 w:2.99739408493042 b:0.586540699005127\n",
            "cost:8.714640617370605 w:2.9978182315826416 b:0.5799477100372314\n",
            "cost:8.714499473571777 w:2.9982216358184814 b:0.5736895799636841\n",
            "cost:8.714378356933594 w:2.9986038208007812 b:0.5677502751350403\n",
            "cost:8.71426773071289 w:2.998966693878174 b:0.5621128678321838\n",
            "cost:8.714173316955566 w:2.9993114471435547 b:0.5567613840103149\n",
            "cost:8.714091300964355 w:2.999638319015503 b:0.5516825914382935\n",
            "cost:8.714018821716309 w:2.9999492168426514 b:0.546862006187439\n",
            "cost:8.713956832885742 w:3.000243902206421 b:0.5422860980033875\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jf4MQcYMypve",
        "colab_type": "text"
      },
      "source": [
        "**this will be predicted result** \n"
      ]
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    {
      "cell_type": "code",
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        "id": "NLei18uByyGe",
        "colab_type": "code",
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      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}