Quick regression analysis example using Tensorflow and the Adam Optimizer

regression-example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "rng = numpy.random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters# Param \n",
    "learning_rate = 0.01\n",
    "training_epochs = 1000\n",
    "display_step = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training Data\n",
    "train_X = numpy.asarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])\n",
    "train_Y = numpy.asarray([356.09, 350.82, 353.14, 358.56, 352.67, 349.44, 343.1, 339.48, 330.53, 326.72, 320.63, 320.51, 326.06, 326.43, 300.66, 300.69, 303.25, 303.26, 304.1, 300.38, 305.11, 307.96, 311.63, 307.0, 314.72, 319.91, 310.13, 313.47, 314.63, 313.71, 313.42, 316.96, 309.62, 307.62, 307.69, 304.44, 304.0, 306.69, 314.84, 314.12, 321.38, 335.74, 340.36, 342.17, 339.83, 342.15, 335.9, 329.07, 330.48, 327.87, 320.26, 314.22, 312.68, 313.21, 316.55, 320.4, 312.12, 314.62, 326.26, 333.1, 333.0, 339.03, 337.04, 339.9, 344.38, 348.02, 346.59, 353.51, 355.75, 349.13, 342.8, 339.86, 344.56, 345.22, 350.69, 354.14, 346.23, 338.74, 329.86, 340.83, 331.61, 307.87, 312.16, 318.35, 322.35, 328.26, 337.38, 336.17, 336.43, 341.1, 351.05, 355.68, 355.0, 348.73, 339.37, 329.1, 333.52, 331.7, 327.12, 329.79, 325.43, 336.86, 340.69, 331.87, 326.98, 323.24, 315.21, 312.5, 316.31, 313.5, 306.03, 299.48, 290.73, 260.39, 259.58, 252.46, 263.92, 270.19, 297.23, 302.39, 299.36, 300.39, 304.32, 298.81, 299.96, 294.34, 287.34, 294.2, 294.78, 294.87, 286.98, 282.77, 281.21, 281.14, 289.15, 295.62, 297.02, 302.32, 281.63, 288.19, 300.56, 303.38, 303.53, 308.55, 303.98, 298.28, 283.73, 285.19, 286.58, 279.32, 286.39, 280.71, 276.95, 278.0, 277.63, 281.32, 288.3, 286.65, 287.89, 296.27, 292.27, 309.83, 321.79, 320.81, 328.58, 346.49, 343.5, 352.68, 357.96, 364.12, 358.13, 358.19, 356.24, 342.13, 332.99, 334.67, 345.14, 351.56, 348.13, 347.31, 321.09, 305.31, 307.03, 313.26, 323.44, 318.5, 318.0, 314.42, 310.71, 316.62, 320.88, 318.77, 317.47, 299.18, 300.13, 302.06, 307.17, 301.52, 291.12, 298.0])\n",
    "n_samples = train_X.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tf Graph Input\n",
    "X = tf.placeholder(\"float\")\n",
    "Y = tf.placeholder(\"float\")\n",
    "\n",
    "# Set model weights\n",
    "W = tf.Variable(rng.randn(), name=\"weight\")\n",
    "b = tf.Variable(rng.randn(), name=\"bias\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct a linear model\n",
    "pred = tf.add(tf.multiply(X, W), b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean squared error\n",
    "cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n",
    "# Gradient descent\n",
    "# optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
    "\n",
    "# Adam Optimizer is used, which is one of the current default optimizers \n",
    "# in deep learning development. Adam stands for “Adaptive Moment Estimation” \n",
    "# and can be considered as a combination between two other popular optimizers \n",
    "# AdaGrad and RMSProp\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the variables (i.e. assign their default value)\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0050 cost= 11212.208984375 W= 1.624199 b= 46.959618\n",
      "Epoch: 0100 cost= 8066.003417969 W= 1.3073506 b= 93.06213\n",
      "Epoch: 0150 cost= 5434.843750000 W= 1.0204656 b= 137.98753\n",
      "Epoch: 0200 cost= 3337.601806641 W= 0.76821214 b= 181.22789\n",
      "Epoch: 0250 cost= 1820.522827148 W= 0.5456723 b= 222.08707\n",
      "Epoch: 0300 cost= 883.478271484 W= 0.34873167 b= 259.24255\n",
      "Epoch: 0350 cost= 439.807617188 W= 0.18674372 b= 289.90445\n",
      "Epoch: 0400 cost= 309.644042969 W= 0.077910334 b= 310.49673\n",
      "Epoch: 0450 cost= 295.321746826 W= 0.021207927 b= 321.24008\n",
      "Epoch: 0500 cost= 300.897033691 W= -0.004233281 b= 326.06818\n",
      "Epoch: 0550 cost= 305.567230225 W= -0.015110233 b= 328.13446\n",
      "Epoch: 0600 cost= 307.959197998 W= -0.01971694 b= 329.00995\n",
      "Epoch: 0650 cost= 309.042053223 W= -0.02165947 b= 329.3792\n",
      "Epoch: 0700 cost= 309.503479004 W= -0.022464473 b= 329.53223\n",
      "Epoch: 0750 cost= 309.719940186 W= -0.022837063 b= 329.6031\n",
      "Epoch: 0800 cost= 309.797424316 W= -0.022970244 b= 329.6284\n",
      "Epoch: 0850 cost= 309.829315186 W= -0.023024794 b= 329.63876\n",
      "Epoch: 0900 cost= 309.843109131 W= -0.023048406 b= 329.64325\n",
      "Epoch: 0950 cost= 309.853973389 W= -0.023067273 b= 329.64682\n",
      "Epoch: 1000 cost= 309.865814209 W= -0.023087274 b= 329.65063\n",
      "Optimization Finished!\n",
      "Training cost= 309.8658 W= -0.023087274 b= 329.65063 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Start training\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "\n",
    "    # Fit all training data\n",
    "    for epoch in range(training_epochs):\n",
    "        for (x, y) in zip(train_X, train_Y):\n",
    "            sess.run(optimizer, feed_dict={X: x, Y: y})\n",
    "\n",
    "        #Display logs per epoch step\n",
    "        if (epoch+1) % display_step == 0:\n",
    "            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})\n",
    "            print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(c), \\\n",
    "                \"W=\", sess.run(W), \"b=\", sess.run(b)\n",
    "\n",
    "    print \"Optimization Finished!\"\n",
    "    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\n",
    "    print \"Training cost=\", training_cost, \"W=\", sess.run(W), \"b=\", sess.run(b), '\\n'\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(\"==> Tomorrow's prediction is:\", 325.033184)\n"
     ]
    }
   ],
   "source": [
    "model_W = -0.02308678\n",
    "model_b = 329.65054\n",
    "tomorrow = model_W * 200 + model_b\n",
    "\n",
    "print('==> Tomorrow\\'s prediction is:', tomorrow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}