deep-learning/Tensorflow-101.ipynb

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   "source": [
    "# Tensorflow 101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taken from Andrew Ng's Coursera Deep Learning series\n",
    "\n",
    "https://www.coursera.org/learn/deep-neural-network/lecture/zcZlH/tensorflow"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "# Implement forward path\n",
    "# TensorFlow figures out backprop\n",
    "w = tf.Variable(0, dtype=tf.float32)\n",
    "cost = tf.add(tf.add(w ** 2, tf.multiply(-10.0, w)), 25)\n",
    "learning_rate = 0.01\n",
    "train = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "session = tf.Session()\n",
    "session.run(init)\n",
    "print(session.run(w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    }
   ],
   "source": [
    "session.run(train)\n",
    "print(session.run(w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.99999\n"
     ]
    }
   ],
   "source": [
    "for _ in range(1000):\n",
    "    session.run(train)\n",
    "print(session.run(w))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative way of identifying the `cost` is to use overloaded operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This is equivalent to\n",
    "# tf.add(tf.add(w ** 2, tf.multiply(-10.0, w)), 25)\n",
    "cost = w ** 2 - 10 * w + 25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training data (with input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.99999\n"
     ]
    }
   ],
   "source": [
    "coefficients = np.array([[1.], [-10.], [25.]])\n",
    "x = tf.placeholder(tf.float32, [3, 1])\n",
    "cost = x[0][0] * w ** 2 + x[1][0] * w + x[2][0]\n",
    "train = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "session = tf.Session()\n",
    "session.run(init)\n",
    "\n",
    "for _ in range(1000):\n",
    "    session.run(train, feed_dict={x: coefficients})\n",
    "print(session.run(w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.99999\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as session:\n",
    "    session.run(init)\n",
    "    for _ in range(1000):\n",
    "        session.run(train, feed_dict={x: coefficients})\n",
    "    print(session.run(w))"
   ]
  }
 ],
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