Linear Regression Tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL']='2'\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import xlrd\n",
    "\n",
    "import python_utils \n",
    "\n",
    "DATA_FILE = 'data/fire_theft.xls'\n",
    "\n",
    "# Phase 1: Assemble the graph\n",
    "# Step 1: read in data from the .xls file\n",
    "book = xlrd.open_workbook(DATA_FILE, encoding_override='utf-8')\n",
    "sheet = book.sheet_by_index(0)\n",
    "data = np.asarray([sheet.row_values(i) for i in range(1, sheet.nrows)])\n",
    "n_samples = sheet.nrows - 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PlaceHolder: A value thats unassigned but will be initialized by the session whenever it is run. Typically placeholders are the input and output of your model. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable: A value that can change such as parameters of ML model. Variables must be initialized by the session before they are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: create placeholders for input X (number of fire) and label Y (number of theft)\n",
    "# Both have the type float32\n",
    "X = tf.placeholder(tf.float32, name='X')\n",
    "Y = tf.placeholder(tf.float32, name='Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: create weight and bias, initialized to 0\n",
    "# name your variables w and b\n",
    "w = tf.Variable(0.0, name='weights')\n",
    "b = tf.Variable(0.0, name='bias')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 4: predict Y (number of theft) from the number of fire\n",
    "# name your variable Y_predicted\n",
    "Y_predicted = X * w + b "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 5: use the square error as the loss function\n",
    "loss = tf.square(Y - Y_predicted, name='loss')\n",
    "# loss = utils.huber_loss(Y, Y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 6: using gradient descent with learning rate of 0.01 to minimize loss\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: 2069.6319333978354\n",
      "Epoch 1: 2117.0123581953535\n",
      "Epoch 2: 2092.302723001866\n",
      "Epoch 3: 2068.5080461938464\n",
      "Epoch 4: 2045.591184088162\n",
      "Epoch 5: 2023.5146448101316\n",
      "Epoch 6: 2002.2447619835536\n",
      "Epoch 7: 1981.748338803649\n",
      "Epoch 8: 1961.9944411260742\n",
      "Epoch 9: 1942.9520116143283\n",
      "Epoch 10: 1924.5930823644712\n",
      "Epoch 11: 1906.8898800636332\n",
      "Epoch 12: 1889.8164505837929\n",
      "Epoch 13: 1873.347133841543\n",
      "Epoch 14: 1857.4588400604468\n",
      "Epoch 15: 1842.1278742424079\n",
      "Epoch 16: 1827.332495119955\n",
      "Epoch 17: 1813.0520579712022\n",
      "Epoch 18: 1799.2660847636982\n",
      "Epoch 19: 1785.9562132299961\n",
      "Epoch 20: 1773.1024853109072\n",
      "Epoch 21: 1760.689129482884\n",
      "Epoch 22: 1748.6984157081515\n",
      "Epoch 23: 1737.1138680398553\n",
      "Epoch 24: 1725.920873066732\n",
      "Epoch 25: 1715.1046249579008\n",
      "Epoch 26: 1704.6500954309377\n",
      "Epoch 27: 1694.5447134910141\n",
      "Epoch 28: 1684.7746311347667\n",
      "Epoch 29: 1675.328450968245\n",
      "Epoch 30: 1666.1935385839038\n",
      "Epoch 31: 1657.3584002084322\n",
      "Epoch 32: 1648.8122658529207\n",
      "Epoch 33: 1640.5440742547091\n",
      "Epoch 34: 1632.5446836102221\n",
      "Epoch 35: 1624.8043315147183\n",
      "Epoch 36: 1617.3126799958602\n",
      "Epoch 37: 1610.0622532456405\n",
      "Epoch 38: 1603.0433557207386\n",
      "Epoch 39: 1596.2479176106197\n",
      "Epoch 40: 1589.668056331575\n",
      "Epoch 41: 1583.2965242617897\n",
      "Epoch 42: 1577.126371285745\n",
      "Epoch 43: 1571.1501190634\n",
      "Epoch 44: 1565.360979151513\n",
      "Epoch 45: 1559.7523780798629\n",
      "Epoch 46: 1554.3184364555138\n",
      "Epoch 47: 1549.0529469620615\n",
      "Epoch 48: 1543.950059985476\n",
      "Epoch 49: 1539.0050282141283\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e97267c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    # Step 7: initialize the necessary variables, in this case, w and b\n",
    "    sess.run(tf.global_variables_initializer()) \n",
    "    \n",
    "    writer = tf.summary.FileWriter('./graphs/linear_reg', sess.graph)\n",
    "    \n",
    "    # Step 8: train the model\n",
    "    for i in range(50): # train the model 100 epochs\n",
    "        total_loss = 0\n",
    "        for x, y in data:\n",
    "# Session runs train_op and fetch values of loss\n",
    "            _, l = sess.run([optimizer, loss], feed_dict={X: x, Y:y}) \n",
    "            total_loss += l\n",
    "        print('Epoch {0}: {1}'.format(i, total_loss/n_samples))\n",
    "\n",
    "    # close the writer when you're done using it\n",
    "    writer.close() \n",
    "    \n",
    "    # Step 9: output the values of w and b\n",
    "    w, b = sess.run([w, b]) \n",
    "\n",
    "# plot the results\n",
    "X, Y = data.T[0], data.T[1]\n",
    "plt.plot(X, Y, 'bo', label='Real data')\n",
    "plt.plot(X, X * w + b, 'r', label='Predicted data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pymongo'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-bc699f3a2d42>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpymongo\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMongoClient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pymongo'"
     ]
    }
   ],
   "source": [
    "from pymongo import MongoClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}