3.1_Linear_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "3.1%20Linear%20Regression.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 2",
      "language": "python",
      "name": "python2"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "L_CkNf-yMWIz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# A linear regression learning algorithm example using TensorFlow library."
      ]
    },
    {
      "metadata": {
        "id": "ssIuhJ9bMWI0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Linear regression model is one of the simplest regression models. It assumes linear relationship between X and Y. The output equation is defined as follows:\n",
        "$$\\hat{y} = WX + b$$\n"
      ]
    },
    {
      "metadata": {
        "id": "7HlKSkOWMWI0",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "22f5eed6-7183-48b0-b373-a129ad8526d3",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969107940,
          "user_tz": -120,
          "elapsed": 1031,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "%matplotlib inline"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YnZrCOZVMWI3",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "764763e6-f0a2-4b21-ad18-e422c91458e0",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969108884,
          "user_tz": -120,
          "elapsed": 831,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "#  Training Parameters\n",
        "learning_rate = 1e-1\n",
        "training_epochs = 2000\n",
        "display_step = 200"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Hb9k9hjLMWI5",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "1dbf5fa5-78c5-45db-e6b3-05920efc6aef",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969109995,
          "user_tz": -120,
          "elapsed": 1005,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Load data. Advertising dataset from \"An Introduction to Statistical Learning\",\n",
        "# textbook by Gareth James, Robert Tibshirani, and Trevor Hastie \n",
        "import numpy as np\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/DataScienceUB/DeepLearningfromScratch2017/master/dataset/Advertising.csv',index_col=0, delimiter=',')\n",
        "train_X = data[['TV']].values \n",
        "\n",
        "train_Y = data.Sales.values \n",
        "train_Y = train_Y[:,np.newaxis]\n",
        "\n",
        "n_samples = train_X.shape[0]\n",
        "print n_samples\n",
        "print train_X.shape, train_Y.shape\n",
        "plt.plot(train_X, train_Y, 'ro', label='Original data')\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200\n",
            "(200, 1) (200, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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L44n1xClK4WIA6XmoeV3b6tRkoOHO4tymmMwl5CU7jTdJjThFKUmUg7vweN7NosyXrkqt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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc5c54be090>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fXYH9klJMWI9",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "e4b2664d-be04-41ab-cb53-3060ebb9ee41",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969111093,
          "user_tz": -120,
          "elapsed": 1012,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "# Define tf Graph Inputs\n",
        "X = tf.placeholder(\"float\",[None,1])\n",
        "y = tf.placeholder(\"float\",[None,1])\n",
        "\n",
        "# Create Model variables \n",
        "# Set model weights\n",
        "W = tf.Variable(np.random.randn(), name=\"weight\")\n",
        "b = tf.Variable(np.random.randn(), name=\"bias\")\n",
        "\n",
        "# Construct a linear model\n",
        "y_pred = tf.add(tf.multiply(X, W), b)\n",
        "\n",
        "# Minimize the squared errors\n",
        "cost = tf.reduce_sum(tf.pow(y_pred-y,2))/(n_samples) #L2 loss\n",
        "\n",
        "# Define the optimizer\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) #Gradient descent"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "id": "q1ugxGZhMWI_",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "f9e499a0-bf05-4ef6-c35b-95043f4d98a6",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969113930,
          "user_tz": -120,
          "elapsed": 2718,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Initializing the variables\n",
        "init = tf.global_variables_initializer()\n",
        "# Launch the graph\n",
        "with tf.Session() as sess:\n",
        "    sess.run(init)\n",
        "\n",
        "    # Fit all training data\n",
        "    for epoch in range(training_epochs):\n",
        "        sess.run(optimizer, feed_dict={X: train_X, y: train_Y})       \n",
        "\n",
        "        #Display logs per epoch step\n",
        "        if epoch % display_step == 0:\n",
        "            print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n",
        "                \"{:.9f}\".format(sess.run(cost, feed_dict={X: train_X, y:train_Y})), \\\n",
        "                \"W=\", sess.run(W), \"b=\", sess.run(b)\n",
        "\n",
        "    print \"Optimization Finished!\"\n",
        "    print \"cost=\", sess.run(cost, feed_dict={X: train_X, y: train_Y}), \\\n",
        "          \"W=\", sess.run(W), \"b=\", sess.run(b)\n",
        "\n",
        "    #Graphic display\n",
        "    plt.plot(train_X, train_Y, 'ro', label='Original data')\n",
        "    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0001 cost= 9454.903320312 W= -0.49233034 b= 0.93602836\n",
            "Epoch: 0201 cost= 15.213056564 W= 0.06944617 b= 2.724759\n",
            "Epoch: 0401 cost= 12.311044693 W= 0.061090976 b= 4.367986\n",
            "Epoch: 0601 cost= 10.993334770 W= 0.054544155 b= 5.6550035\n",
            "Epoch: 0801 cost= 10.603803635 W= 0.05058813 b= 6.4327087\n",
            "Epoch: 1001 cost= 10.524839401 W= 0.048652414 b= 6.8132467\n",
            "Epoch: 1201 cost= 10.513781548 W= 0.04787619 b= 6.9658427\n",
            "Epoch: 1401 cost= 10.512723923 W= 0.047621492 b= 7.0159125\n",
            "Epoch: 1601 cost= 10.512657166 W= 0.04755375 b= 7.02923\n",
            "Epoch: 1801 cost= 10.512653351 W= 0.047539372 b= 7.0320573\n",
            "Optimization Finished!\n",
            "cost= 10.512653 W= 0.04753697 b= 7.0325284\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vdHSZiS984qj7AuD3sF/BN1jZEt9v/3s0fvuJ6GNr4y0I\ngiBoY+ewiSAIgqCDGG9BEAQHIsZbEATBgYjxFgRBcCBivAVBEByIGG9BEAQHIsZbEATBgfw/MqwZ\nMyKuHnEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc5bacfadd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "IWFE0DscMWJC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Multiple Linear Regression\n",
        "Let's use three features as input vector : TV,Radio,Newspaper"
      ]
    },
    {
      "metadata": {
        "id": "5hqjoRy0MWJC",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "682b3a93-b13e-4596-bf4d-c440ac2a9bc6",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969115028,
          "user_tz": -120,
          "elapsed": 1040,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Parameters\n",
        "learning_rate = 1e-2\n",
        "training_epochs = 2000\n",
        "display_step = 200"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nEOeXulQMWJE",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "520b9f95-257d-4ad3-9da4-063a8392e93d",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969117015,
          "user_tz": -120,
          "elapsed": 1885,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/DataScienceUB/DeepLearningfromScratch2017/master/dataset/Advertising.csv',index_col=0, delimiter=',')\n",
        "train_X = data[['TV','Radio','Newspaper']].values\n",
        "train_Y = data.Sales.values \n",
        "train_Y = train_Y[:,np.newaxis]\n",
        "n_samples = train_X.shape[0]\n",
        "print n_samples\n",
        "print train_X.shape, train_Y.shape\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200\n",
            "(200, 3) (200, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Gp1MDEAJMWJH",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "c1990625-12d6-4fde-d5a9-4c2b9e3ccb42",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969117702,
          "user_tz": -120,
          "elapsed": 588,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Define tf Graph Inputs\n",
        "X = tf.placeholder(\"float\",[None,3])\n",
        "y = tf.placeholder(\"float\",[None,1])\n",
        "\n",
        "# Create Model variables \n",
        "# Set model weights\n",
        "W = tf.Variable(tf.zeros([3, 1]),name=\"bias\")\n",
        "b = tf.Variable(np.random.randn(), name=\"bias\")\n",
        "\n",
        "# Construct a linear model\n",
        "y_pred = tf.matmul(X, W) + b"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RHY--sUfMWJJ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "8111b393-2e22-42bc-892c-dc4061fd4f15",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969118399,
          "user_tz": -120,
          "elapsed": 597,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Minimize the squared errors\n",
        "cost = tf.reduce_sum(tf.pow(y_pred-y,2))/(2*n_samples) #L2 loss\n",
        "\n",
        "# Define the optimizer\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) #Gradient descent"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zUCPPqt7MWJM",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "6b43f222-383f-406f-e206-4fc995f25b90",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969120031,
          "user_tz": -120,
          "elapsed": 1531,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Initializing the variables\n",
        "init = tf.global_variables_initializer()\n",
        "# Launch the graph\n",
        "with tf.Session() as sess:\n",
        "    sess.run(init)\n",
        "\n",
        "    # Fit all training data\n",
        "    for epoch in range(training_epochs):\n",
        "        sess.run(optimizer, feed_dict={X: train_X, y: train_Y})\n",
        "        \n",
        "        #Display logs per epoch step\n",
        "        if epoch % display_step == 0:\n",
        "            print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n",
        "                \"{:.9f}\".format(sess.run(cost, feed_dict={X: train_X, y:train_Y})), \\\n",
        "                \"W=\", sess.run(W), \"b=\", sess.run(b)\n",
        "\n",
        "    print \"Optimization Finished!\"\n",
        "    print \"cost=\", sess.run(cost, feed_dict={X: train_X, y: train_Y}), \\\n",
        "          \"W=\", sess.run(W), \"b=\", sess.run(b)\n",
        "    "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0001 cost= 86.326835632 W= [[0.01]\n",
            " [0.01]\n",
            " [0.01]] b= -0.35885254\n",
            "Epoch: 0201 cost= 2.007852077 W= [[0.05367266]\n",
            " [0.22218329]\n",
            " [0.01653813]] b= 0.03463026\n",
            "Epoch: 0401 cost= 1.816062450 W= [[0.05232672]\n",
            " [0.21644714]\n",
            " [0.01354946]] b= 0.52897114\n",
            "Epoch: 0601 cost= 1.649169564 W= [[0.05087486]\n",
            " [0.21026522]\n",
            " [0.01032231]] b= 1.0622686\n",
            "Epoch: 0801 cost= 1.530633807 W= [[0.0495164 ]\n",
            " [0.20448473]\n",
            " [0.00730258]] b= 1.5611866\n",
            "Epoch: 1001 cost= 1.458390474 W= [[0.04836035]\n",
            " [0.19956744]\n",
            " [0.00473274]] b= 1.9857281\n",
            "Epoch: 1201 cost= 1.420098186 W= [[0.04745222]\n",
            " [0.19570553]\n",
            " [0.002714  ]] b= 2.3192055\n",
            "Epoch: 1401 cost= 1.402437091 W= [[0.04679121]\n",
            " [0.19289485]\n",
            " [0.00124457]] b= 2.5619335\n",
            "Epoch: 1601 cost= 1.395387888 W= [[0.04634581]\n",
            " [0.19100101]\n",
            " [0.00025444]] b= 2.7254863\n",
            "Epoch: 1801 cost= 1.392974496 W= [[ 0.04606894]\n",
            " [ 0.18982378]\n",
            " [-0.00036105]] b= 2.827155\n",
            "Optimization Finished!\n",
            "cost= 1.3922757 W= [[ 0.04591151]\n",
            " [ 0.18915443]\n",
            " [-0.00071103]] b= 2.8849626\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "umIU80meMWJO",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d28e771b-15f4-45f0-d68c-2d02cae51c83",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528969121038,
          "user_tz": -120,
          "elapsed": 945,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 20,
      "outputs": []
    }
  ]
}