1_Linear_Regression/01-LinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select tensorflow 1.x (colab only)\n",
    "%tensorflow_version 1.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Data simulation\n",
    "y = 1*x + 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = np.linspace(0,10,20) + np.random.uniform(-1.5,1.5,20)\n",
    "y_data = np.linspace(0,10,20) + np.random.uniform(-1.5,1.5,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb311a6a58>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_data, y_data, '*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initiallize w and b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = np.random.uniform(-1,1)\n",
    "b = np.random.uniform(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7015595835039523 0.9884520548622662\n"
     ]
    }
   ],
   "source": [
    "print(w,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_tf = tf.Variable(w)\n",
    "b_tf = tf.Variable(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = 0\n",
    "for x,y in zip(x_data,y_data):\n",
    "    error += (w_tf * x + b_tf - y)**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)\n",
    "train = optimizer.minimize(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_steps = 10\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    for step in range(train_steps):\n",
    "        sess.run(train)\n",
    "    w_final, b_final = sess.run([w_tf,b_tf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = w_final * x_data + b_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb31991b70>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_data, y_data, '*')\n",
    "plt.plot(x_data, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (spinningup)",
   "language": "python",
   "name": "spinningup"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}