lab-01-basics.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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   "source": [
    "# Getting Started With TensorFlow\n",
    "https://www.tensorflow.org/get_started/get_started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check TF version"
   ]
  },
  {
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   "execution_count": 1,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hello TensorFlow!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Hello, TensorFlow!'\n"
     ]
    }
   ],
   "source": [
    "# Create a constant op\n",
    "# This op is added as a node to the default graph\n",
    "hello = tf.constant(\"Hello, TensorFlow!\")\n",
    "\n",
    "# start a TF session\n",
    "sess = tf.Session()\n",
    "\n",
    "# run the op and get result\n",
    "print(sess.run(hello))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[1.0, 2.0, 3.0]], [[7.0, 8.0, 9.0]]]"
      ]
     },
     "execution_count": 3,
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   "source": [
    "3 # a rank 0 tensor; this is a scalar with shape []\n",
    "[1. ,2., 3.] # a rank 1 tensor; this is a vector with shape [3]\n",
    "[[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]\n",
    "[[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computational Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "node1 = tf.constant(3.0, tf.float32)\n",
    "node2 = tf.constant(4.0) # also tf.float32 implicitly\n",
    "node3 = tf.add(node1, node2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node1: Tensor(\"Const_1:0\", shape=(), dtype=float32) node2: Tensor(\"Const_2:0\", shape=(), dtype=float32)\n",
      "node3:  Tensor(\"Add:0\", shape=(), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(\"node1:\", node1, \"node2:\", node2)\n",
    "print(\"node3: \", node3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Graph](https://www.tensorflow.org/images/getting_started_adder.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sess.run(node1, node2):  [3.0, 4.0]\n",
      "sess.run(node3):  7.0\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "print(\"sess.run(node1, node2): \", sess.run([node1, node2]))\n",
    "print(\"sess.run(node3): \", sess.run(node3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.5\n",
      "[ 3.  7.]\n"
     ]
    }
   ],
   "source": [
    "a = tf.placeholder(tf.float32)\n",
    "b = tf.placeholder(tf.float32)\n",
    "adder_node = a + b  # + provides a shortcut for tf.add(a, b)\n",
    "\n",
    "print(sess.run(adder_node, feed_dict={a: 3, b: 4.5}))\n",
    "print(sess.run(adder_node, feed_dict={a: [1,3], b: [2, 4]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.5\n"
     ]
    }
   ],
   "source": [
    "add_and_triple = adder_node * 3.\n",
    "print(sess.run(add_and_triple, feed_dict={a: 3, b:4.5}))"
   ]
  }
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