3.1 Linear Regression.ipynb

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   "source": [
    "# A linear regression learning algorithm example using TensorFlow library."
   ]
  },
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   "cell_type": "markdown",
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   "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"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "#  Training Parameters\n",
    "learning_rate = 1e-1\n",
    "training_epochs = 2000\n",
    "display_step = 200"
   ]
  },
  {
   "cell_type": "code",
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   "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('dataset/Advertising.csv',index_col=0)\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()"
   ]
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  {
   "cell_type": "code",
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   "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.mul(X, W), b)"
   ]
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    "# 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"
   ]
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    "# Initializing the variables\n",
    "init = tf.initialize_all_variables()\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()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### Multiple Linear Regression\n",
    "Let's use three features as input vector : TV,Radio,Newspaper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Parameters\n",
    "learning_rate = 1e-2\n",
    "training_epochs = 2000\n",
    "display_step = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data = pd.read_csv('dataset/Advertising.csv',index_col=0)\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Initializing the variables\n",
    "init = tf.initialize_all_variables()\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",
    "    "
   ]
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