linear_ANN.ipynb

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  {
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   "source": [
    "## Simple Linear ANN Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create some data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# Create 100 x, y data points y = x * 0.1 + 0.3\n",
    "x_data = np.random.rand(100).astype(np.float32)\n",
    "y_data = x_data * 0.1 + 0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine Structure of the Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Try to find values for W and b that compute y_data = W * x_data + b\n",
    "# (We know that W should be 0.1 and b 0.3, but Tensorflow will\n",
    "# figure that out for us.)\n",
    "W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))\n",
    "b = tf.Variable(tf.zeros([1]))\n",
    "y = W * x_data + b\n",
    "\n",
    "# Minimize the mean squared errors.\n",
    "loss = tf.reduce_mean(tf.square(y - y_data))\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.5)\n",
    "train = optimizer.minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize & Launch Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Before starting, initialize the variables.  We will 'run' this first.\n",
    "init = tf.initialize_all_variables()\n",
    "\n",
    "# Launch the graph.\n",
    "sess = tf.Session()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [ 0.61219382] [ 0.04213925]\n",
      "100 [ 0.10063908] [ 0.29967076]\n",
      "200 [ 0.10000081] [ 0.29999959]\n",
      "300 [ 0.10000011] [ 0.29999995]\n",
      "400 [ 0.10000011] [ 0.29999995]\n"
     ]
    }
   ],
   "source": [
    "# Fit the line.\n",
    "for step in range(500):\n",
    "    sess.run(train)\n",
    "    if step % 100 == 0:\n",
    "        print(step, sess.run(W), sess.run(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Final answer should be [.1] and [.3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final: [ 0.10000011] [ 0.29999995]\n"
     ]
    }
   ],
   "source": [
    "print('Final: ' + str(sess.run(W)) + \" \" + str(sess.run(b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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