ML_Lab01_IntroToTensorFlow.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow Hello World!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.13.1'"
      ]
     },
     "execution_count": 3,
     "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": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Hello, Tensorflow!'\n"
     ]
    }
   ],
   "source": [
    "# Create a constant op (node)\n",
    "# This op is added as a node to the default graph\n",
    "hello = tf.constant(\"Hello, Tensorflow!\")\n",
    "\n",
    "# seart a TF sesstion\n",
    "sess = tf.Session()\n",
    "\n",
    "# run the op (node) and get reasult\n",
    "print(sess.run(hello))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "constant node를 하나 생성하고, 빈 세션을 생성한 뒤,  \n",
    "그 세션에서 실행하는 방식.  \n",
    "각 node는 연산을 의미함. \n",
    "  \n",
    "b는 byte를 의미. 즉 위에서는 byte string의 의미를 가진다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computational Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow Mechanics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) Build graph using TensorFlow Operations  \n",
    "(2) Feed data and run graph (operation)  \n",
    "    -> **sess.run(op)**  or **sess.run(op, feed_dict={x:x_data})**  \n",
    "(3) Update variables in the graph (and return value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) Build graph using TensorFlow Operations  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node1: Tensor(\"Const_3:0\", shape=(), dtype=float32) \n",
      "node2: Tensor(\"Const_4:0\", shape=(), dtype=float32)\n",
      "node3: Tensor(\"Add:0\", shape=(), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(\"node1:\", node1, \"\\nnode2:\", node2)\n",
    "print(\"node3:\", node3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) Feed data and run graph (operation) \n",
    "### (3) Update variables in the graph (and return value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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": "markdown",
   "metadata": {},
   "source": [
    "session을 실행시켜야 실행이된다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Placeholder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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 shorcut for tf.add(a, b)\n",
    "\n",
    "#sess = tf.Session()\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": "markdown",
   "metadata": {},
   "source": [
    "Placeholder는 실행을 하면서 값을 받는 개념. 자리를 미리 잡아 두는것.  \n",
    "변수 개념이라 생각하면 될까?  \n",
    "그래프를 만들어놓고, 실행할 때 데이터를 feed 해주는 것.\n",
    "feed_dict에서 값을 넘겨준다.  \n",
    "\n",
    "**sess.run(op, feed_dict={x: x_data})**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Everything is Tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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]"
   ]
  }
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